Entries |
Document | Title | Date |
20080201278 | Method and Apparatus for Automatic Online Detection and Classification of Anomalous Objects in a Data Stream - The invention is concerned with a method for automatic online detection and classification of anomalous objects in a data stream, especially comprising datasets and/or signals, wherein a) the detection of at least one incoming data stream containing normal and anomalous objects, b) automatic construction of a geometric representation of normality the incoming objects of the data stream at a time t | 08-21-2008 |
20080201279 | METHOD AND APPARATUS FOR AUTOMATICALLY STRUCTURING FREE FORM HETERGENEOUS DATA - Techniques are provided for automatically structuring free form heterogeneous data. In one aspect of the invention, the techniques include obtaining free form heterogeneous data, segmenting the free form heterogeneous data into one or more units, automatically labeling the one or more units based on one or more machine learning techniques, wherein each unit is associated with a label indicating an information type, and structuring the one or more labeled units in a format to facilitate one or more operations that use at least a portion of the labeled units, e.g., information technology (IT) operations. | 08-21-2008 |
20080201280 | MEDICAL ONTOLOGIES FOR MACHINE LEARNING AND DECISION SUPPORT - A medical ontology may be used for computer assisted clinical decision support. Multi-level and/or semantically grouped medical ontology is incorporated into a machine learning algorithm. The resulting machine-learnt algorithm outputs information to assist in clinical decisions. For example, a patient record is input to the algorithm. Based on the incorporated medical ontology, similarities are aggregated in different groups. An aggregate similarity of at least one group is a function of an aggregate similarity of at least another group. One or more similar patients and/or outcomes are identified based on similarity. Probability based outputs may be provided. | 08-21-2008 |
20080201281 | PARALLEL SUPPORT VECTOR METHOD AND APPARATUS - Disclosed is an improved technique for training a support vector machine using a distributed architecture. A training data set is divided into subsets, and the subsets are optimized in a first level of optimizations, with each optimization generating a support vector set. The support vector sets output from the first level optimizations are then combined and used as input to a second level of optimizations. This hierarchical processing continues for multiple levels, with the output of each prior level being fed into the next level of optimizations. In order to guarantee a global optimal solution, a final set of support vectors from a final level of optimization processing may be fed back into the first level of the optimization cascade so that the results may be processed along with each of the training data subsets. This feedback may continue in multiple iterations until the same final support vector set is generated during two sequential iterations through the cascade, thereby guaranteeing that the solution has converged to the global optimal solution. In various embodiments, various combinations of inputs may be used by the various optimizations. The individual optimizations may be processed in parallel. | 08-21-2008 |
20080208773 | Multi-core stochastic discrimination - In some embodiments, multi-core stochastic discrimination is generally presented. In this regard, a method is introduced comprising providing random regions of a feature space to parallel cores, testing each random region for enrichment in parallel, recording coverage for each data point in each enriched random region in parallel, and calculating an overall average coverage for each data point among the enriched random regions. Other embodiments are also disclosed and claimed. | 08-28-2008 |
20080208774 | ONTOLOGY SYSTEM FOR CONTEXT-AWARE, METHOD THEREOF, AND RECORDING MEDIUM STORING THE SAME - Provided are an ontology system, a method for managing the ontology system, and a recording medium storing the same. The ontology system includes: a context broker unit for receiving context information from a sensing device and verifying a validity of the received context information; a context managing unit for controlling to generate an ontology structure by transforming the verified context information from the context broker unit to ontology web language (OWL) data and processing the OWL data; a rule-based inference engine unit for transforming the processed context information from the context managing unit to semantic web rule language (SWRL) data and processing the SWRL data through an inference process; a learning managing unit for processing the processed context information from the context managing unit through learning; and a database for storing the context information processed at the context managing unit, the rule-based engine unit, and the learning managing unit. | 08-28-2008 |
20080208775 | Method and Apparatus for Generation of a Sequence of Elements - A method and apparatus for automatically generating a target sequence of a plurality of elements selected in accordance with a plurality of user-defined constraints such as a play list of songs. The apparatus comprises a user interface ( | 08-28-2008 |
20080208776 | METHOD AND APPARATUS FOR LEARNING BEHAVIOR IN SOFTWARE ROBOT - Disclosed is a method and apparatus for learning behavior in a software robot. The method includes detecting a kind of an object in cyberspace related to a kind of presently manifested action, and a kind and the variation of at least one state among percept states or emotional states preset so as to change in relation to the kind of the action; finding episodes respectively corresponding to each of one or more objects in the cyberspace, each of one or more emotional states and each of one or more percept states, respectively defined in the software robot, a kind of an object in cyberspace related to the detected kind of the action among multiple episodes for responding a combination of kinds of respective one or more actions and for storing variation related to each state, and a kind of at least one state among percept states or emotional states preset so as to change in relation to the kind of the action; using variation stored in response to the found episode and variation generated in response to the manifested action, and calculating a representative variation; and storing the representative variation as a variation of the found episode. | 08-28-2008 |
20080208777 | METHODS AND APPARATUS FOR PREDICTIVE ANALYSIS - Methods and apparatus for predictive analytics generally comprise one or more artificial agents and an agent factory. An artificial agent may be responsive to at least one of an internal data set and an external data set. Further, an artificial agent may produce a correlation data set relating an outcome data set and at least one of the internal data set and the external data set. In addition, an artificial agent may produce a predictability value corresponding to the correlation data set. The agent factory may be responsive to the outcome data set. Also, the agent factory may produce the artificial agent in response to the outcome data set. | 08-28-2008 |
20080208778 | CONTROLLING A NON-LINEAR PROCESS - System and method for modeling a nonlinear process. A combined model for predictive optimization or control of a nonlinear process includes a nonlinear approximator, coupled to a parameterized dynamic or static model, operable to model the nonlinear process. The nonlinear approximator receives process inputs, and generates parameters for the parameterized dynamic model. The parameterized dynamic model receives the parameters and process inputs, and generates predicted process outputs based on the parameters and process inputs, where the predicted process outputs are useable to analyze and/or control the nonlinear process. The combined model may be trained in an integrated manner, e.g., substantially concurrently, by identifying process inputs and outputs (I/O), collecting data for process I/O, determining constraints on model behavior from prior knowledge, formulating an optimization problem, executing an optimization algorithm to determine model parameters subject to the determined constraints, and verifying the compliance of the model with the constraints. | 08-28-2008 |
20080215510 | MULTI-SCALE SEGMENTATION AND PARTIAL MATCHING 3D MODELS - A scale-Space feature extraction technique is based on recursive decomposition of polyhedral surfaces into surface patches. The experimental results show that this technique can be used to perform matching based on local model structure. Scale-space techniques can be parameterized to generate decompositions that correspond to manufacturing, assembly or surface features relevant to mechanical design. One application of these techniques is to support matching and content-based retrieval of solid models. Scale-space technique can extract features that are invariant with respect to the global structure of the model as well as small perturbations that | 09-04-2008 |
20080215511 | SYSTEM AND METHOD FOR AUTOMATED PART-NUMBER MAPPING - Automated mapping of part numbers associated with parts in a bill of materials (BOM) submitted by a BOM originator to internal part numbers assigned to those parts by a BOM receiver is performed by one or more computers connected to one or more networks through one or more network interfaces. A first receive component receives one or more data sets containing historical data on bills of materials received in the past by the BOM receiver. A second receive component receives one or more data sets containing known mappings between internal part numbers used by the BOM receiver, and part numbers used by various BOM originators. A third receive component receives one or more data sets containing information of various parameters and their values describing the parts to which the BOM receiver has assigned internal part numbers. A fourth receive component receives one or more methods of automatically learning models for predicting internal part numbers from the above mentioned historical BOM data, mapping data and part parametric data. A learning component learns the models from the data. A fifth receive component receives a BOM from a requesting process. The BOM has one or more parts with a missing internal part number. A mapping component applies the learned models to the received BOM to automatically determine internal part numbers for all unmapped BOM originator part numbers. A release process assigns internal part numbers to all unmapped parts in the BOM and releases the BOM to the requesting process. | 09-04-2008 |
20080222058 | Bayesian-network-based method and system for detection of clinical-laboratory errors - Embodiments of the present invention include methods and systems for analyzing clinical-laboratory results and data in order to detect erroneous clinical-laboratory results. Embodiments of the present invention employ Bayesian networks and modified Bayesian networks that are constructed using cleaned clinical-laboratory results into which various types of synthetic errors have been introduced and that are optimized using different, cleaned clinical-laboratory results into which synthetic errors have been introduced. | 09-11-2008 |
20080222059 | COMPUTER-IMPLEMENTED METHOD, COMPUTER PROGRAM AND SYSTEM FOR ANALYZING DATA RECORDS - A computer implemented method and system for analysing a first set of data records where each data record comprises attribute values for one or more attributes, by expanding the first set of data records into a second set of data records by creating for at least one of the attributes of the first set of data records at least two redundant attributes with corresponding redundant attribute values, assigning different generalization rules to the at least two redundant attributes, and performing a generalization of the second set of data records by means of an attribute-oriented induction (AOI)-algorithm. | 09-11-2008 |
20080222060 | SYSTEM AND METHOD OF MINING TIME-CHANGING DATA STREAMS USING A DYNAMIC RULE CLASSIFIER HAVING LOW GRANULARITY - A dynamic rule classifier for mining a data stream includes at least one window for viewing data contained in the data stream and a set of rules for mining the data. Rules are added and the set of rules are updated by algorithms when an drift in a concept within the data occurs, causing unacceptable drops in classification accuracy. The dynamic rule classifier is also implemented as a method and a computer program product. | 09-11-2008 |
20080228676 | Computing device, method of controlling the computing device, and computer readable medium recording a program - A computing device stores a Bayesian network ( | 09-18-2008 |
20080235163 | SYSTEM AND METHOD FOR ONLINE DUPLICATE DETECTION AND ELIMINATION IN A WEB CRAWLER - As part of the normal crawling process, a crawler parses a page and computes a de-tagged hash, called a fingerprint, of the page content. A lookup structure consisting of the host hash (hash of the host portion of the URL) and the fingerprint of the page is maintained. Before the crawler writes a page to a store, this lookup structure is consulted. If the lookup structure already contains the tuple (i.e., host hash and fingerprint), then the page is not written to the store. Thus, a lot of duplicates are eliminated at the crawler itself, saving CPU and disk cycles which would otherwise be needed during current duplicate elimination processes. | 09-25-2008 |
20080235164 | Apparatus, method and computer program product providing a hierarchical approach to command-control tasks using a brain-computer interface - Disclosed is a method, a computer program product, and a device that are responsive to detected mental states of a user to perform selection processes to execute a task. The method includes providing a hierarchical multi-level decision tree structure comprised of internal nodes and leaf nodes, where the decision tree structure represents a task. The method further includes navigating, using information derived from detected mental states of the user, through levels of the decision tree structure to reach a leaf node to accomplish the task. The step of navigating includes selecting, using the information derived from the detected mental states of the user, between attribute values associated with internal nodes of the decision tree structure. As non-limiting examples, the device may be a communication device, and the task may be a name dialing or a command/control task. | 09-25-2008 |
20080235165 | Weak hypothesis generation apparatus and method, learning aparatus and method, detection apparatus and method, facial expression learning apparatus and method, facial enpression recognition apparatus and method, and robot apparatus - A facial expression recognition system that uses a face detection apparatus realizing efficient learning and high-speed detection processing based on ensemble learning when detecting an area representing a detection target and that is robust against shifts of face position included in images and capable of highly accurate expression recognition, and a learning method for the system, are provided. When learning data to be used by the face detection apparatus by Adaboost, processing to select high-performance weak hypotheses from all weak hypotheses, then generate new weak hypotheses from these high-performance weak hypotheses on the basis of statistical characteristics, and select one weak hypothesis having the highest discrimination performance from these weak hypotheses, is repeated to sequentially generate a weak hypothesis, and a final hypothesis is thus acquired. In detection, using an abort threshold value that has been learned in advance, whether provided data can be obviously judged as a non-face is determined every time one weak hypothesis outputs the result of discrimination. If it can be judged so, processing is aborted. A predetermined Gabor filter is selected from the detected face image by an Adaboost technique, and a support vector for only a feature quantity extracted by the selected filter is learned, thus performing expression recognition. | 09-25-2008 |
20080235166 | TRAINING A MODEL OF A NON-LINEAR PROCESS - System and method for modeling a nonlinear process. A combined model for predictive optimization or control of a nonlinear process includes a nonlinear approximator, coupled to a parameterized dynamic or static model, operable to model the nonlinear process. The nonlinear approximator receives process inputs, and generates parameters for the parameterized dynamic model. The parameterized dynamic model receives the parameters and process inputs, and generates predicted process outputs based on the parameters and process inputs, where the predicted process outputs are useable to analyze and/or control the nonlinear process. The combined model may be trained in an integrated manner, e.g., substantially concurrently, by identifying process inputs and outputs (I/O), collecting data for process I/O, determining constraints on model behavior from prior knowledge, formulating an optimization problem, executing an optimization algorithm to determine model parameters subject to the determined constraints, and verifying the compliance of the model with the constraints. | 09-25-2008 |
20080243728 | Recursive Feature Eliminating Method Based on a Support Vector Machine - Method, apparatus and system are described to perform a feature eliminating method based on a support vector machine. In some embodiments, a value for each feature in a group of features provided by a training data is determined. At least one feature is eliminated from the group by utilizing the value for each feature in the group. The value for each feature in the group is updated based upon a part of the training data that corresponds to the eliminated feature. | 10-02-2008 |
20080243729 | LEVERAGING USER-TO-USER INTERACTIONS IN A KNOWLEDGEBASE USING A FORUM INTERFACE - Systems and methods provide a self-learning knowledgebase in which the ranking and/or order of topic and thread items may be dynamically and automatically adjusted based on self-learning by the knowledgebase. The knowledgebase includes threaded conversations comprising thread topics and thread items within the thread topics. Lists of thread topics and lists of thread items are ordered lists. The order of a thread topic or thread item in an ordered list may be modified based on self-learning activities performed by an information server maintaining the knowledge base. A thread topic or thread item may be moved higher in the list based on requests to view the thread topic or thread item. Further, the order that a thread item appears in an order list may be modified based on a number of responses posted for the thread item. | 10-02-2008 |
20080243730 | Training a machine learning system to determine photoresist parameters - To train a machine learning system, a set of different values of one or more photoresist parameters, which characterize behavior of photoresist when the photoresist undergoes processing steps in a wafer application, is obtained. A set of diffraction signals is obtained using the set of different values of the one or more photoresist parameters. The machine learning system is trained using the set of measured diffraction signals as inputs to the machine learning system and the set of different values of the one or more photoresist parameters as expected outputs of the machine learning system. | 10-02-2008 |
20080243731 | GENERALIZED SEQUENTIAL MINIMAL OPTIMIZATION FOR SVM+ COMPUTATIONS - A system and method for support vector machine plus (SVM+) computations include selecting a set of indexes for a target function to create a quadratic function depending on a number of variables, and reducing the number of variables to two in the quadratic function using linear constraints. An extreme point is computed for the quadratic function in closed form. A two-dimensional set is defined where the indexes determine whether a data point is in the two-dimensional set or not. A determination is made of whether the extreme point belongs to the two-dimensional set. If the extreme point belongs to the two-dimensional set, the extreme point defines a maximum and defines a new set of parameters for a next iteration. Otherwise, the quadratic function is restricted on at least one boundary of the two-dimensional set to create a one-dimensional quadratic function. The steps are repeated until the maximum is determined. | 10-02-2008 |
20080262984 | Field-Programmable Gate Array Based Accelerator System - Accelerator systems and methods are disclosed that utilize FPGA technology to achieve better parallelism and flexibility. The accelerator system may be used to implement a relevance-ranking algorithm, such as RankBoost, for a training process. The algorithm and related data structures may be organized to enable streaming data access and, thus, increase the training speed. The data may be compressed to enable the system and method to be operable with larger data sets. At least a portion of the approximated RankBoost algorithm may be implemented as a single instruction multiple data streams (SIMD) architecture with multiple processing engines (PEs) in the FPGA. Thus, large data sets can be loaded on memories associated with an FPGA to increase the speed of the relevance ranking algorithm. | 10-23-2008 |
20080262985 | SYSTEMS, METHODS, AND MEDIA FOR GENERATING SANITIZED DATA, SANITIZING ANOMALY DETECTION MODELS, AND/OR GENERATING SANITIZED ANOMALY DETECTION MODELS - Systems, methods, and media for generating sanitized data, sanitizing anomaly detection models, and generating anomaly detection models are provided. In some embodiments, methods for generating sanitized data are provided. The methods including: dividing a first training dataset comprised of a plurality of training data items into a plurality of data subsets each including at least one training data item of the plurality of training data items of the first training dataset; based on the plurality of data subsets, generating a plurality of distinct anomaly detection micro-models; testing at least one data item of the plurality of data items of a second training dataset of training data items against each of the plurality of micro-models to produce a score for the at least one tested data item; and generating at least one output dataset based on the score for the at least one tested data item. | 10-23-2008 |
20080262986 | METHOD FOR TRAINING A CLASSIFIER - A method for training a classifier which forms part of a search engine comprises: receiving a document submitted by an end user of the search engine at a server; creating a training set of documents, the training set including the document submitted by the end user; training the classifier using the training set; and paying an incentive to the end user for submitting the document. | 10-23-2008 |
20080262987 | OBTAINING A VALUE VIA A RULE ENGINE IMPLEMENTED BY A COLLECTION OBJECT - A system and computer program product for obtaining a value via a rule engine implemented by a collection object associated with an object-oriented application. A request for the value includes a key, is received from the application, and is directed to a method of the collection object. The collection object is capable of storing the key and associated data, and providing the data in response to receiving the request. An overriding of the method of the collection object replaces the provision of the data by the collection object with a processing of the request by a rule engine external to the application. The rule is identified in a rule definition file external to the application based on an association between the rule and the key. An algorithm associated with the rule and included in the rule definition file is executed to provide the requested value. | 10-23-2008 |
20080270328 | Building and Using Intelligent Software Agents For Optimizing Oil And Gas Wells - A system and method for monitoring processes in the production of oil and gas uses intelligent software agents employing associative memory techniques that receive data from sensors in the production environment and from other sources and perform pattern matching operations to identify normal and abnormal behavior of the well production. The agents report the behaviors to human operators or other software systems. The abnormal behavior may consist of any behavior of the production processes that is other than the desired behavior of the well. The intelligent software agents are trained to identify both specific behaviors and behaviors that have never before been observed and recognized in the well. | 10-30-2008 |
20080270329 | SYSTEMS AND METHODS FOR MARTINGALE BOOSTING IN MACHINE LEARNING - Boosting algorithms are provided for accelerated machine learning in the presence of misclassification noise. In an exemplary embodiment, a machine learning method having multiple learning stages is provided. Each learning stage may include partitioning examples into bins, choosing a base classifier for each bin, and assigning an example to a bin by counting the number of positive predictions previously made by the base classifier associated with the bin. | 10-30-2008 |
20080275827 | Parallelization of Bayesian Network Structure Learning - A master computing node directs parallel structure learning with intelligent computational task distribution. The master computing node may determine what families are to be used to score neighbors in a neighbor scoring process, and determine if the families have scores in a score cache. Families to be scored for the score cache may be marked and distributed for calculation among nodes in the computing cluster. The score cache may be updated to include the scored families, and the cluster synchronized with the score cache data. | 11-06-2008 |
20080281764 | Machine Learning System - A method for training a classifier to classify elements of a data set according to a characteristic is described. The data set includes N elements with the elements each characterized by at least one feature. The method includes the steps of forming a first labeled subset of elements from the data set with the elements of the first labeled subset each labeled according to whether the element includes the characteristic, training an algorithmic classifier to classify for the characteristic according to the first labeled subset thereby determining which at least one feature is relevant to classifying for the characteristic; and then querying with the classifier an inverted index, with this inverted index formed over the at least one feature and generated from the data set, thereby generating a ranked set of elements from the data set. | 11-13-2008 |
20080281765 | Method of Ranking Politically Exposed Persons and Other Heightened Risk Persons and Entities - A method for ranking politically exposed persons and/or other persons and entities that pose a heightened risk based on their importance wherein an exposure index is determined for each person in the population as a function of the existence or absence of a relationship with each of the other members of the population and each of one or more exposure factors such as position held by the person, country in which the position is held, and source of information about the person. The politically exposed persons in the population are ranked in accordance with their respective exposure indexes. The population is sorted and a subset of the population containing those politically exposed persons having exposure indexes indicative or the highest likelihood of illicit financial activity is thereby identified. | 11-13-2008 |
20080281766 | Time Machine Software - A method and system for creating human robots with psychic abilities, as well as enabling a human robot to access information in a time machine to predict the future accurately and realistically. The present invention provides a robot with the ability to accomplish tasks quickly and accurately without using any time. This permits a robot to cure cancer, fight a war, write software, read a book, learn to drive a car, draw a picture or solve a complex math problem in less than one second. | 11-13-2008 |
20080288424 | Apparatus, Method, and Computer Program Product Providing Improved Identification of Suspect Entries in Transaction Data - The exemplary embodiments of the invention provide apparatus, systems, methods and computer program products for scoring entities in order to use the scoring for such tasks as identifying and prioritizing those entities that are candidates for further investigation, for example, from an audit or business control perspective. In an exemplary aspect of the invention, a method includes: providing transaction data having a plurality of pieces of information and an identification of a corresponding entity of a plurality of entities, wherein at least one piece of information of the plurality of pieces of information corresponds to each entity of the plurality of entities, wherein the transaction data comprises input data; computing at least one score for each entity of the plurality of entities by applying at least one statistical analysis technique to the input data, wherein the computed at least one score for a tested entity is indicative of at least one of a magnitude of deviation of the tested entity from a determined normal and repeated abnormal behavior of the tested entity; selecting zero or more entities of the plurality of entities by comparing at least one computed score of each entity with a specified threshold, wherein the selected zero or more entities comprise candidates for further investigation; and ordering the selected zero or more entities based on at least one computed score of each entity of the selected zero or more entities. | 11-20-2008 |
20080288425 | Methods and Apparatus for Reasoning About Information Fusion Approaches - Methods and apparatus for reasoning about information fusion approaches are described according to some aspects. In one aspect, a method for reasoning about information fusion approaches comprises selecting one or more information fusion approaches for evaluation, and applying the approaches to two or more sets of data. The selection of information fusion approaches can be based on a taxonomy that classifies information fusion algorithms. The impact of each of the applied information fusion approaches can then be conveyed to a user, wherein the conveyed impact is based on one or more impact measures. | 11-20-2008 |
20080294577 | Efficient Estimation of Events with Rare Occurrence Rates Using Taxonomies - Methods for predicting the click-through rates of Internet advertisements placed into web pages are disclosed. Specifically, a click-through rate prediction is generating using a hybrid system with two terms. The first term is constructed using a machine learning model that incorporates a limited number of important factors. The second term is constructed using a look-up table that is built using a complex statistical analysis of various web page and advertisement combinations. To construct the second term, the field of multi-level hierarchical modeling is used. Specifically, a tree-structured Markov model is used to process the training data and construct the adjustment factor look-up table. To reduce the complexity of the statistical analysis, Kalman-filters are used to estimate parameters in the traditional multi-level hierarchical models for scalability. | 11-27-2008 |
20080294578 | DIAGNOSING INTERMITTENT FAULTS - A method and system for diagnosing any combination of persistent and intermittent faults. The behavior of a system under test is obtained by measuring or probing the system at a particular location(s). The predicted behavior of a modeled system corresponding to the system under test is investigated by drawing inferences based on at least conditional probabilities, prior observations and component models. The predictions are compared to their corresponding points in the system under test. A determination is made if a conflict exists between the measured behavior and the predicted behavior, and the conditional probabilities are adjusted to more and more accurately reflect the action fault(s) in the system under test. The conflicts or deviations between the obtained predicted behavior and the actual behavior are used to isolate the components of the system causing the faults. | 11-27-2008 |
20080294579 | LOW-POWER ANALOG-CIRCUIT ARCHITECTURE FOR DECODING NEURAL SIGNALS - A microchip for performing a neural decoding algorithm is provided. The microchip is implemented using ultra-low power electronics. Also, the microchip includes a tunable neural decodable filter implemented using a plurality of amplifiers, a plurality of parameter learning filters, a multiplier, a gain and time-constant biasing circuits; and analog memory. The microchip, in a training mode, learns to perform an optimized translation of a raw neural signal received from a population of cortical neurons into motor control parameters. The optimization being based on a modified gradient descent least square algorithm wherein update for a given parameter in a filter is proportional to an averaged product of an error in the final output that the filter affects and a filtered version of its input. The microchip, in an operational mode, issues commands to controlling a device using learned mappings. | 11-27-2008 |
20080301069 | SYSTEM AND METHOD FOR LEARNING BALANCED RELEVANCE FUNCTIONS FROM EXPERT AND USER JUDGMENTS - The present invention relates to systems and methods for determining a content item relevance function. The method comprises collecting user preference data at a search provider for storage in a user preference data store and collecting expert-judgment data at the search provider for storage in an expert sample data store. A modeling module trains a base model through the use of the expert-judgment data and tunes the base model through the use of the user preference data to learn a set of one or more tuned models. A measure (B measure) is designed to evaluate the balanced performance of tuned model over expert judgment and user preference. The modeling module generates or selects the content item relevance function from the tuned models with B measure as the selection criterion. | 12-04-2008 |
20080301070 | KERNELS AND METHODS FOR SELECTING KERNELS FOR USE IN LEARNING MACHINES - Learning machines, such as support vector machines, are used to analyze datasets to recognize patterns within the dataset using kernels that are selected according to the nature of the data to be analyzed. Where the datasets possesses structural characteristics, locational kernels can be utilized to provide measures of similarity among data points within the dataset. The locational kernels are then combined to generate a decision function, or kernel, that can be used to analyze the dataset. Where an invariance transformation or noise is present, tangent vectors are defined to identify relationships between the invariance or noise and the data points. A covariance matrix is formed using the tangent vectors, then used in generation of the kernel. | 12-04-2008 |
20080301071 | Support Vector Inductive Logic Programming - A computer implemented method of particular, although not exclusive application to analysing a plurality of molecules which comprises computing a kernel function for each pair of the plurality of molecules, the kernel function being representative of the number of features present in both molecules of the pairs and using the kernel function in a kernel based learning algorithm to model the relationship between the features and a property of the molecules. The method is also applicable to predicting a numerical value representing a characteristic of a molecule and, more generally, modelling instances of data in a database. A particular, although again not exclusive application, is the prediction of toxicity of a molecule. | 12-04-2008 |
20080301072 | ROBOT SIMULATION APPARATUS - A robot simulation apparatus including: a display section which displays models of at least a conveyance apparatus, an object, and a robot respectively laid out at predetermined positions; a movement condition designating section which designates a direction and a speed of movement of the object; a imaging condition designating section which designates a relative position of the camera with respect to the object and imaging condition in order to obtain a still image of the object located within an imaging area; a teaching model storage section which stores a teaching model of the object to be compared with the still image obtained with the camera; a grasping position calculating section which calculates a grasping position of the object to be grasped by the robot based on a position and an attitude of the object obtained by comparing the still image with the teaching model, and on the direction and the speed of movement of the object; and a teaching position setting section which sets a teaching position for said robot based on the grasping position. | 12-04-2008 |
20080306887 | METHOD FOR MACHINE LEARNING WITH STATE INFORMATION - Methods, systems, and computer program products are provided for the online convex optimization problem, in which the decision maker has knowledge of the all past states and resulting cost functions for his previous choices and attempts to make a new choice that results in minimum regret. The method does not rely upon the structure of the cost function or the characterization of the states and takes advantage of the similarity between successive states to enable the method to converge to a reasonably optimal result. | 12-11-2008 |
20080306888 | STOCHASTIC CONTROL OPTIMIZATION FOR SENDER-BASED FLOW CONTROL IN A DISTRIBUTED STATEFUL MESSAGING SYSTEM - A method and system for controlling message flow in distributed stream processing. State transition probabilities in a Markov model having one state per staleness value of data are determined for sending or withholding updates of data to subscribers using expected message rates from an information provider. A cost function annotates each state transition in the model with a state transition cost for each decision to “send” or “withhold”. A propagation policy specifying whether to send or withhold the message is determined for each state. The propagation policy is then deployed. If a new message comprising an update of data is received during a lapsed time unit, a staleness value of the data held by subscribers is increased. The propagation policy is used to determine whether to send or withhold the message. If the message should be sent, the message is propagated and the staleness value of the data is reset. | 12-11-2008 |
20080306889 | SYSTEM FOR SUPPORTING USER'S BEHAVIOR - Provided is a system | 12-11-2008 |
20080306890 | Plant Control Apparatus - A plant control system includes: a numerical calculation execution part which calculates the operation characteristic of the plant; a model for simulating the plant control characteristic according to information on the numerical calculation result; a learning part which learns the plant operation method by using the model; a learning information database which stores learning information data on the learning part; a pattern generation part which generates pattern data expressing a state input based on the learning information data in the learning part with a smaller input number than the model input dimension; a pattern database which stores the pattern data generated in the pattern generation part; and a learning result determination part which selects a learning result having a preferable control effect from the learning result obtained by using a plurality of patterns. | 12-11-2008 |
20080306891 | METHOD FOR MACHINE LEARNING WITH STATE INFORMATION - Methods, systems, and computer program products are provided for the online convex optimization problem, in which the decision maker has knowledge of the all past states and resulting cost functions for his previous choices and attempts to make a new choice that results in minimum regret. The method does not rely upon the structure of the cost function or the characterization of the states and takes advantage of the similarity between successive states to enable the method to converge to a reasonably optimal result. | 12-11-2008 |
20080313109 | SYSTEM, METHOD AND COMPUTER PROGRAM PRODUCT FOR EVELUATING A STORAGE POLICY - A method for generating a storage policy, the method includes: receiving a storage system target function; and generating, by a machine learning entity, the storage policy in response to: (a) a set of file-related storage operation requests, (b) a state of the storage system before responding to the set of file-related storage operation requests, and (c) the storage system target function. A method for evaluating a storage policy, the method includes: simulating an application of the storage policy by the storage system during a first period, in response to a set of file-related storage operation requests that was provided to the storage system during the first period, to provide a simulation result; wherein the first period starts before the simulating. | 12-18-2008 |
20080313110 | METHOD AND SYSTEM FOR SELF-CALIBRATING PROJECT ESTIMATION MODELS FOR PACKAGED SOFTWARE APPLICATIONS - An estimation system for deriving multi-dimensional project plans for implementing packaged software applications with self-calibration and refinement of project estimation models, the system includes: a view layer configured to act as a user interface for user inputs and system outputs; a model and control layer configured to implement rules based on a series of estimation and implementation models, and to perform self-calibration and refinement of project estimation models for multi-dimensional project plans; an estimation knowledge base layer configured to hold and derive the series of estimation and implementation models; and wherein the system for self-calibration and refinement of project estimation models for multi-dimensional project plans for implementing packaged software applications is carried out over networks comprising: the Internet, intranets, local area networks (LAN), and wireless local area networks (WLAN). | 12-18-2008 |
20080313111 | LARGE SCALE ITEM REPRESENTATION MATCHING - A two-phase process quickly and accurately identifies representations of the same items within a collection of item representations. In the first phase, referred to as a “blocking phase,” frequency information indicating the frequency with which terms appear within the collection of item representations is used to quickly identify “candidate pairs” (i.e., pairs of item representations that have a relatively high probability of matching). The blocking phase results in a reduced subset of the data for further analysis during the second phase. In the second phase, referred to as a “matching phase,” the candidate pairs are analyzed using fuzzy matching functions to accurately identify “matching pairs” (i.e., representations of the same items). | 12-18-2008 |
20080313112 | LEARNING MACHINE THAT CONSIDERS GLOBAL STRUCTURE OF DATA - A new machine learning technique is herein disclosed which generalizes the support vector machine framework. A separating hyperplane in a separating space is optimized in accordance with generalized constraints which dependent upon the clustering of the input vectors in the dataset. | 12-18-2008 |
20090006282 | USING A DATA MINING ALGORITHM TO GENERATE RULES USED TO VALIDATE A SELECTED REGION OF A PREDICTED COLUMN - Provided are an article of manufacture, system, and method for using a data mining algorithm to generate rules used to validate a selected region of a predicted column. A data set has a plurality of columns and records providing data for each of the columns. Selection is received of at least one predicted column for which rules are to be generated and at least one region of the selected at least one predicted column, wherein each region specifies data positions in the column. The data set is processed to determine association relationships among data in at least one predictor column and subsequences in the selected at least one region of the at least one predicted column. At least one rule is generated from the relationships specifying a condition involving at least one predictor column that predicts at least one value in the selected region of the at least one predicted column. | 01-01-2009 |
20090006283 | USING A DATA MINING ALGORITHM TO GENERATE FORMAT RULES USED TO VALIDATE DATA SETS - Provided are a method, system, and article of manufacture for using a data mining algorithm to generate format rules used to validate data sets. A data set has a plurality of columns and records providing data for each of the columns. Selection is received of at least one format column for which format rules are to be generated and selection is received of at least one predictor column. A format mask column is generated for each selected format column. For records in the data set, a value in the at least one format column is converted to a format mask representing a format of the value in the format column and storing the format mask in the format mask column in the record for which the format mask was generated. The at least one predictor column and the at least one format mask column are processed to generate at least one format rule. Each format rule specifies a format mask associated with at least one condition in the at least one predictor column. | 01-01-2009 |
20090006284 | FORECASTING TIME-INDEPENDENT SEARCH QUERIES - Techniques for analyzing and modeling the frequency of queries are provided by a query analysis system. A query analysis system analyzes frequencies of a query over time to determine whether the query is time-dependent or time-independent. The query analysis system forecasts the frequency of time-dependent queries based on their periodicities. The query analysis system forecasts the frequency of time-independent queries based on causal relationships with other queries. To forecast the frequency of time-independent queries, the query analysis system analyzes the frequency of a query over time to identify significant increases in the frequency, which are referred to as “query events” or “events.” The query analysis system forecasts frequencies of time-independent queries based on queries with events that tend to causally precede events of the query to be forecasted. | 01-01-2009 |
20090006285 | CONTENT-BASED TAGGING OF RSS FEEDS AND E-MAIL - Providing for automated generation of tags (e.g., metadata descriptors) for items of e-mail or syndication formatted communication is described herein. By way of example, a system can include a filtering component that can generate one or more tags based on information relevant to content of the communication, a sender, or recipient, or combinations thereof. In addition, such tags can be automatically attached to a received item, or a presentation component can furnish the tags to a recipient (e.g., by way of a communication device user interface) for selection, whereby selected tags are associated with the item of communication. Accordingly, the subject innovation provides for improved classification and description of items of communication by automatic generation of descriptive and/or representative tags associated therewith. | 01-01-2009 |
20090006286 | METHOD AND APPARATUS FOR IMPLEMENTING DIGITAL VIDEO MODELING TO IDENTIFY UNEXPECTED BEHAVIOR - A computer implemented method, apparatus, and computer usable program product for identifying unexpected behavioral patterns. The process parses event data derived from video data to identify behavioral patterns, wherein the event data comprises metadata describing events occurring in a selected environment. The process analyzes the behavioral patterns to identify a set of expected behavioral patterns occurring in the selected environment, and generates an expected behavioral model using the expected behavioral patterns. Thereafter, the process forms a set of unexpected behavioral patterns from the behavioral patterns inconsistent with the expected behavioral model. | 01-01-2009 |
20090006287 | Kernel machine approximation - Systems and methods associated with approximating a kernel matrix are described. One method embodiment includes accessing a set of kernel machine training data and then partitioning that data into a set of partitioned data based on a set of partition parameters. The method embodiment may also include creating a partition matrix from the partitioned data, where the partition matrix is to approximate a kernel matrix that may have been created using radial basis functions from the training data. | 01-01-2009 |
20090006288 | Information Processing Apparatus, Information Processing Method, and Program - An information processing apparatus learning a preference of a user for a content item includes acquiring means for acquiring an operation or expression of the user for a certain content item as feedback information; training data generating means for generating training data for the preference learning from the feedback information acquired by the acquiring means; and learning means for learning the preference of the user and how to attach a meaning to the feedback information in association with the training data by using multiple pieces of training data generated by the training data generating means. | 01-01-2009 |
20090006289 | Hierarchical Temporal Memory System with Enhanced Inference Capability - A node, a computer program storage medium, and a method for a hierarchical temporal memory (HTM) network where at least one of its nodes generates a top-down message and sends the top-down message to one or more children nodes in the HTM network. The first top-down message represents information about the state of a node and functions as feedback information from a current node to its child node. The node may also maintain history of the input patterns or co-occurrences so that temporal relationships between input patterns or co-occurrences may be taken into account in an inference stage. By providing the top-town message and maintaining history of previous input patterns, the HTM network may, among others, (i) perform more accurate inference based on temporal history, (ii) make predictions, (iii) discriminate between spatial co-occurrences with different temporal histories, (iv) detect “surprising” temporal patterns, (v) generate examples from a category, and (vi) fill in missing or occluded data. | 01-01-2009 |
20090012919 | EXPLAINING CHANGES IN MEASURES THRU DATA MINING - Systems and methodologies for identification of factors that cause significant shifts in transactions in a relational store and/or OLAP environment. Transactions are grouped into significant categories defined across the whole data space, to detect interesting sub spaces transactions. Subsequently, sub spaces that show strong variance between two slices can be selected, followed by grouping the subspaces in sub reports to measure the coverage for each sub report. A final report can then be generated that contains list of sub-reports detected in the previous acts. | 01-08-2009 |
20090012920 | Human Artificial Intelligence Software Program - A method of creating human artificial intelligence in machines and computer software is presented here, as well as methods to simulate human reasoning, thought and behavior. The present invention serves as a universal artificial intelligence program that will store, retrieve, analyze, assimilate, predict the future and modify information in a manner and fashion which is similar to human beings and which will provide users with a software application that will serve as the main intelligence of one or a multitude of computer based programs, software applications, machines or compilation of machinery. | 01-08-2009 |
20090012921 | METHOD FOR IDENTIFYING A PERSON'S POSTURE - A classification method including first classifying an event of any kind by first rules, and then second classifying events, not identified by the first classification, by a learning base reinforced with all the events identified by the first classification. The method is adaptive if the second classification rules are amended according to new examples that were able to be determined by the first rules. | 01-08-2009 |
20090012922 | METHOD AND APPARATUS FOR REWARD-BASED LEARNING OF IMPROVED SYSTEMS MANAGEMENT POLICIES - In one embodiment, the present invention is a method for reward-based learning of improved systems management policies. One embodiment of the inventive method involves supplying a first policy and a reward mechanism. The first policy maps states of at least one component of a data processing system to selected management actions, while the reward mechanism generates numerical measures of value responsive to particular actions (e.g., management actions) performed in particular states of the component(s). The first policy and the reward mechanism are applied to the component(s), and results achieved through this application (e.g., observations of corresponding states, actions and rewards) are processed in accordance with reward-based learning to derive a second policy having improved performance relative to the first policy in at least one state of the component(s). | 01-08-2009 |
20090018979 | MATH PROBLEM CHECKER - A problem checker architecture that monitors user progress during a problem-solving process and assists the user through the process (e.g., when requested) using common human methods of solving the problem. Assistance can be in the form of detecting errors during the process, and providing context-sensitive help information when the user gets stuck or makes a mistake. The problem checker can walk the user through the process of solving a math problem one step at a time allowing the user to learn to solve math problems according to a number of different methods. Rather than simply calculating and displaying the answer, the problem checker allows the user to attempt to solve math problems, providing direction only when asked and correction only when required. The problem checker can recognize multiple solution methods for many common math problems and guide the user to the solution via any of the methods. | 01-15-2009 |
20090018980 | MULTIPLE-INSTANCE PRUNING FOR LEARNING EFFICIENT CASCADE DETECTORS - A “Classifier Trainer” trains a combination classifier for detecting specific objects in signals (e.g., faces in images, words in speech, patterns in signals, etc.). In one embodiment “multiple instance pruning” (MIP) is introduced for training weak classifiers or “features” of the combination classifier. Specifically, a trained combination classifier and associated final threshold for setting false positive/negative operating points are combined with learned intermediate rejection thresholds to construct the combination classifier. Rejection thresholds are learned using a pruning process which ensures that objects detected by the original combination classifier are also detected by the combination classifier, thereby guaranteeing the same detection rate on the training set after pruning. The only parameter required throughout training is a target detection rate for the final cascade system. In additional embodiments, combination classifiers are trained using various combinations of weight trimming, bootstrapping, and a weak classifier termed a “fat stump” classifier. | 01-15-2009 |
20090018981 | LEARNING CLASSIFIERS USING COMBINED BOOSTING AND WEIGHT TRIMMING - A “Classifier Trainer” trains a combination classifier for detecting specific objects in signals (e.g., faces in images, words in speech, patterns in signals, etc.). In one embodiment “multiple instance pruning” (MIP) is introduced for training weak classifiers or “features” of the combination classifier. Specifically, a trained combination classifier and associated final threshold for setting false positive/negative operating points are combined with learned intermediate rejection thresholds to construct the combination classifier. Rejection thresholds are learned using a pruning process which ensures that objects detected by the original combination classifier are also detected by the combination classifier, thereby guaranteeing the same detection rate on the training set after pruning. The only parameter required throughout training is a target detection rate for the final cascade system. In additional embodiments, combination classifiers are trained using various combinations of weight trimming, bootstrapping, and a weak classifier termed a “fat stump” classifier. | 01-15-2009 |
20090018982 | SEGMENTED MODELING OF LARGE DATA SETS - To provide efficient and effective modeling of data set, the data set is initially separated into several subsets which can then be processed independently. The subsets themselves are chosen to have some internal commonality, thus providing effective independent tools where possible. This commonality may include correlation between variables or interaction amongst the variables in the subset. Once separated, each subset is independently modeled, creating a subset model having predictive qualities related to the data subset. Next, the subset models themselves are aggregated to generate a overall final model. This final model is predictive of outcomes based upon all data in the data set, thus providing a more robust stable model. | 01-15-2009 |
20090018983 | METHOD AND SYSTEM FOR DETECTING ANOMALOUS PROCESS BEHAVIOR - A method for learning a process behavior model based on a process past instances and on one or more process attributes, and a method for detecting an anomalous process using the corresponding process behavior model. | 01-15-2009 |
20090018984 | System and method for dynamic knowledge construction - A system and method responsive to input stimuli is provided by incorporating a computer software program, hardware processing engine, or a specialized ASIC chip processor apparatus to capture concurrent inputs that are responsive to training stimulation, store a model representing a synthesis of the captured inputs, and use the stored model to generate outputs in response to real-world stimulation. Human user forced-choice approval/disapproval generated descriptions and decisions may be dynamically mapped with conventionally presented information and sensor and control data. The model mapping is stored into and out of a conventional mass storage device, such as is used in a relational database for use in generating a response to the stimuli. By accessing commonly stored mappings, the system can be incorporated into a mixture of multiple domains and disciplines of users and can create a common understanding of knowledge and design concept contained within it through mutual interaction, and subsequent automatic modifications to a common relational database. The system and method is applicable to conventional storage and presentation devices, making it easily incorporated into a variety of commercial products, utilizing current commercial human-machine interfaces (e.g. Human-Machine Interface graphical user interface, or Graphical User Interface) and current mass storage devices. The system uses N-dimensional descriptions of observations and concepts in an infinitely expandable space, embracing elements of human thought. This allows the user to tailor this system to control operation of automated devices and appliances to reflect the individual's wishes and desires as a dynamic representation and mapping of user descriptions and decisions with information, sensor data, and device controls. | 01-15-2009 |
20090024546 | SYSTEM, METHOD AND APPARATUS FOR PREDICTIVE MODELING OF SPATIALLY DISTRIBUTED DATA FOR LOCATION BASED COMMERCIAL SERVICES - A computer system implements a method to provide a class membership probability prediction based on collected usage data from a user device of a user. After device usage data, which contains location information, is collected from the user device, the collected usage data is processed to generate a predictive model by utilizing a machine learning algorithm. In response to a user input, a class membership probability estimation is produced by processing the user input through the probability predictive model. The resulted class membership probability estimation can then be used as a prediction of a demographic profile of the user. | 01-22-2009 |
20090030857 | MULTIATTRIBUTE SPECIFICATION OF PREFERENCES ABOUT PEOPLE, PRIORITIES, AND PRIVACY FOR GUIDING MESSAGING AND COMMUNICATIONS - The present invention relates to a system and methodology to facilitate multiattribute adjustments and control associated with messages and other communications and informational items that are directed to a user via automated systems. An interface, specification language, and controls are provided for defining a plurality of variously configured groups that may attempt to communicate respective items. Controls include the specification of priorities and preferences as well as the modification of priorities and preferences that have been learned from training sets via machine learning methods. The system provides both a means for assessing parameters used in the control of messaging and communications and for the inspection and modification of parameters that have been learned autonomously. | 01-29-2009 |
20090037351 | System and Method to Enable Training a Machine Learning Network in the Presence of Weak or Absent Training Exemplars - Described is a system and method for training a machine learning network. The method comprises initializing at least one of nodes in a machine learning network and connections between the nodes to a predetermined strength value, wherein the nodes represent factors determining an output of the network, providing a first set of questions to a plurality of users, the first set of questions relating to at least one of the factors, receiving at least one of choices and guesstimates from the users in response to the first set of questions and adjusting the predetermined strength value as a function of the choices/guesstimates. The real and simulated examples presented demonstrate that synthetic training sets derived from expert or non-expert human guesstimates can replace or augment training data sets comprised of actual training exemplars that are too limited in size, scope, or quality to otherwise generate accurate predictions. | 02-05-2009 |
20090043715 | Method to Continuously Diagnose and Model Changes of Real-Valued Streaming Variables - The method trains an inductive model to output multiple models from the inductive model and trains an error correlation model to estimate an average output of predictions made by the multiple models. Then the method can determine an error estimation of each of the multiple models using the error correlation model. | 02-12-2009 |
20090043716 | DATA CLASSIFICATION METHOD AND APPARATUS - A data classification apparatus for classifying plural input data into plural categories, in which the apparatus includes a prototype select unit for selecting the prototype of the category nearest to the input data that has been read, a prototype evaluation unit for evaluating whether the selected prototype is proper, a prototype addition unit for adding a prototype in the case where the selected prototype is not proper and an internal data correcting unit for correcting at least one of the prototype and an area determining parameter specifying the size of the category area for each category in the case where the selected prototype is proper. The size of the category area can be set for each category, and therefore, the data can be properly classified and the judgment accuracy is improved in an application to fault detection and fault diagnosis. | 02-12-2009 |
20090043717 | METHOD AND A SYSTEM FOR SOLVING DIFFICULT LEARNING PROBLEMS USING CASCADES OF WEAK LEARNERS - A method and a system for designing a learning system ( | 02-12-2009 |
20090048990 | Temporal Document Trainer and Method - An electronic document sorter is trained to classify documents based on their temporal qualities. The invention can be used in environments such as automated news aggregators, search engines and other electronic systems which compile information having temporal qualities. | 02-19-2009 |
20090055332 | METHOD OF GENERATING ASSOCIATION RULES FROM DATA STREAM AND DATA MINING SYSTEM - Disclosed is a method and data mining system for generating association rules from a data stream. An embodiment of the invention provides a method of generating association rules from a data stream, which is a non-limited data set composed of transactions continuously generated. The method includes: when itemsets included in the generated transactions and the counts of the itemsets are managed using a prefix tree and each node of the prefix tree has information on the count of a specific itemset corresponding to the node and a specific item, updating the information of a node corresponding to the itemset or adding a new node on the basis of the itemset included in the generated transaction and the count of the itemset; comparing the support of the itemset corresponding to each of the nodes of the prefix tree with a minimum support, which is a predetermined threshold value, to select frequent itemsets; and visiting all or some of the nodes corresponding to the selected frequent itemsets, and generating the association rule on the basis of the information of each of the visited nodes. | 02-26-2009 |
20090063374 | Category Classification Method - A category classification method includes: calculating function values corresponding to a relationship between a classification target and support vectors that contribute to a classification boundary, calculating an addition value in which the function value for each support vector has been added, and classifying that the classification target does not pertain to a specific category in case that the addition value is smaller than a threshold, wherein calculation of the addition value is carried out by adding function values having positive values, then adding function values having negative values, and the classification target is classified as not pertaining to the specific category, without adding the remaining function values, in case that the addition value has become smaller than the threshold. | 03-05-2009 |
20090063375 | SYSTEM AND METHOD FOR COMPILING RULES CREATED BY MACHINE LEARNING PROGRAM - A system, a method, and a machine-readable medium are provided. A group of linear rules and associated weights are provided as a result of machine learning. Each one of the group of linear rules is partitioned into a respective one of a group of types of rules. A respective transducer for each of the linear rules is compiled. A combined finite state transducer is created from a union of the respective transducers compiled from the linear rules. | 03-05-2009 |
20090070279 | ESTIMATING THE EFFICACY OF A MATHEMATICAL MODEL OF SYSTEM BEHAVIOUR - Estimating the overall efficacy of a mathematical model of system behaviour involves providing a template representing factors that affect the overall efficacy of the mathematical model. A Bayesian Belief Network (BBN) having nodes based on the factors of the template is created and the BBN is used to obtain an estimate of the overall efficacy of the mathematical model of system behaviour. | 03-12-2009 |
20090076988 | Method and system for optimal choice - A method and system for optimal choice is described. An inductive database system uses an integration of historical data and virtual data (in the form of intuitive rule-sets specified by an agent or plurality of agents) to make statistical recommendations for optimal choice. Filter mechanisms support the reporting of choice recommendations and user interaction with historical data. In the latter case, user interaction with a deductive interface allows for the testing of decision criteria or rule-sets against an historical database and empirical target results. The constant testing of ideas against an objective function provides an update methodology for a database of virtual data and provides a training methodology for the user. An example of picking stock investments is given. | 03-19-2009 |
20090083199 | PROCESSING DEVICE HAVING SELECTIBLE LIST ITEMS WITH INTUITIVE LEARNING CAPABILITY - A processing device and a method of providing learning capability thereto are provided. A list containing a plurality of listed items with an associated item probability distribution is generated. The item probability distribution comprises a plurality of probability values corresponding to the plurality of listed items. One or more items are selected from the plurality of listed items based on the item probability distribution, a performance index indicative of a performance of the processing device relative to the objective is determined, and the item probability distribution is modified based on the performance index. | 03-26-2009 |
20090089225 | WEB-BASED VISUALIZATION MASH-UPS FOR INDUSTRIAL AUTOMATION - A visualization system that generates visual mash-ups for industrial automation includes a ash-up component that combines output from a subset of disparate sources into a common interface. The disparate sources include at least one of equipment, computers, or devices within an industrial automation environment. A visualization component generates and displays a mash-up visualization that includes information associated with the common interface. | 04-02-2009 |
20090089226 | VISUALIZATION OF NON-TIME SERIES EVENTS - Systems and methods that displays available relationships between internal and external data streams. A coordination component can collect and analyze both the “internal” data stream(s) and the “external” data stream(s) simultaneously, and a visualization component can present a form of a visual cue, on a collection of history data and network data. Accordingly, instead of merely storing data values as function of time, other non-time series correlation states can be employed dynamically to represent data to the user. | 04-02-2009 |
20090089227 | AUTOMATED RECOMMENDATIONS FROM SIMULATION - An industrial controller simulation system is provided. The system includes a simulation component that enables modeling of an industrial controller system. A suggestion component offers automated recommendations in accordance with the modeling of the industrial controller system. | 04-02-2009 |
20090089228 | GENERALIZED REDUCED ERROR LOGISTIC REGRESSION METHOD - A machine classification learning method titled Generalized Reduced Error Logistic Regression (RELR) is presented. The method overcomes significant limitations in prior art logistic regression and other machine classification learning methods. The method is applicable to all current applications of logistic regression, but has significantly greater accuracy using smaller sample sizes and larger numbers of input variables than other machine classification learning methods including prior art logistic regression. | 04-02-2009 |
20090094174 | METHOD, SYSTEM AND PROGRAM PRODUCT FOR ON DEMAND DATA MINING SERVER WITH DYNAMIC MINING MODELS - The present invention in various implementations provides a method, system and computer program product for dynamically determining data mining results using a dynamic data mining model within a data mining system. The present invention, in accordance with various implementations, in part, creates a mining model for an event request that includes a plurality of mining rule sets determined in relation to the event and one or more business objectives and selected computations. | 04-09-2009 |
20090094175 | INTRUSIVE SOFTWARE MANAGEMENT - Intrusion features of a landing page associated with sponsored content are identified. A feature score for the landing page based on the identified intrusion features is generated, and if the feature score for the landing page exceeds a feature threshold, the landing page is classified as a candidate landing page. A sponsor account associated with the candidate landing page can be suspended, or sponsored content associated with the candidate landing page can be suspended. | 04-09-2009 |
20090094176 | PANDEMIC REMOTE ACCESS DESIGN - In one example embodiment, a system and method is illustrated that includes receiving user count information that includes a user count value and an address identifier. Further, an operation is executed that includes using the user count information to determine whether a limit variable has been exceeded. An operation is executed that removes a member, identified by the address identifier, from a load balancing pool, where the limit variable has been exceed by the user count information. A further operation is shown that includes introducing a device into the load balancing pool, where the user count information is less than or equal to the difference between the limit variable value and a buffer variable. | 04-09-2009 |
20090099983 | SYSTEM AND METHOD FOR AUTHORING AND LEARNING - Provided is a training method comprising delivering a situation to a learner, the situation comprising an event; automatically inserting a still menu in order to simulate a pause in the delivering; while pausing, recording an action by the learner in response to the event; reviewing the action recorded by the learner; and presenting to the learner a preferred action by a master in response to the event. Also provided is an authoring method comprising recording a situation comprising an event; further recording a master preferred action in response to the event; creating a program comprising motion menus and still menus where the situation, event, and master preferred action are placed on motion menus and still menus; and arranging the program and recordings so that the program carries out the following: delivering the recorded situation to the learner; automatically inserting a still menu after completing the play of a motion menu in order to simulate a pause in the delivering; recording the learner's action in response to the event; reviewing the learner's action; and presenting the learner with the master preferred action. An authoring and learning system is also provided, the system comprising a master video recording tool; a program comprising at least two motion menus and at least two still menus; a learner video delivery tool; a delivery medium; a learner video recording tool; and a learner control. The system may further comprise a program wherein the program gives an author flexibility to set up videos on alternating motion menus, and still menus and integrated recording and reviewing functions among the alternating menus, and wherein the program gives a learner some interactive control over the alternating menus and recording and reviewing functions. | 04-16-2009 |
20090099984 | SYSTEMS AND METHODS FOR GENERATING PREDICTIVE MATRIX-VARIATE T MODELS - Systems and methods predict missing elements from a partially-observed matrix by receiving one or more user item ratings; generating a model parameterized by matrices U, S, V; and outputting the model. | 04-16-2009 |
20090099985 | METHOD AND APPARATUS FOR IMPROVED REWARD-BASED LEARNING USING ADAPTIVE DISTANCE METRICS - The present invention is a method and an apparatus for reward-based learning of policies for managing or controlling a system or plant. In one embodiment, a method for reward-based learning includes receiving a set of one or more exemplars, where at least two of the exemplars comprise a (state, action) pair for a system, and at least one of the exemplars includes an immediate reward responsive to a (state, action) pair. A distance metric and a distance-based function approximator estimating long-range expected value are then initialized, where the distance metric computes a distance between two (state, action) pairs, and the distance metric and function approximator are adjusted such that a Bellman error measure of the function approximator on the set of exemplars is minimized. A management policy is then derived based on the trained distance metric and function approximator. | 04-16-2009 |
20090099986 | LEARNING TRADEOFFS BETWEEN DISCRIMINATIVE POWER AND INVARIANCE OF CLASSIFIERS - Systems and methods are described for learning the discriminative power-invariance tradeoffs for classification of input data (“tradeoff learning system”). In various embodiments, the tradeoff learning system receives multiple classifiers (“base classifiers”) and employs a learning technique to produce a combined classifier. Each received base classifier achieves a different level of tradeoff. The learning technique then decreases a function of kernel weights associated with each of the received classifiers to produce the combined classifier. By decreasing the function of kernel weights, the tradeoff learning system computes a combined classifier that classifies input data more accurately than the received multiple classifiers. | 04-16-2009 |
20090106172 | FALSE DISCOVERY RATE FOR GRAPHICAL MODLES - The claimed subject matter provides systems and/or methods that determines a number of non-spurious arcs associated with a learned graphical model. The system can include devices and mechanisms that utilize learning algorithms and datasets to generate learned graphical models and graphical models associated with null permutations of the datasets, ascertaining the average number of arcs associated with the graphical models associated with null permutations of the datasets, enumerating the total number of arcs affiliated with the learned graphical model, and presenting a ratio of the average number of arcs to the total number of arcs, the ratio indicative of the number of non-spurious arcs associated the learned graphical model. | 04-23-2009 |
20090106173 | LIMITED-MEMORY QUASI-NEWTON OPTIMIZATION ALGORITHM FOR L1-REGULARIZED OBJECTIVES - An algorithm that employs modified methods developed for optimizing differential functions but which can also handle the special non-differentiabilities that occur with the L | 04-23-2009 |
20090106174 | METHODS, SYSTEMS, AND COMPUTER PROGRAM PRODUCTS EXTRACTING NETWORK BEHAVIORAL METRICS AND TRACKING NETWORK BEHAVIORAL CHANGES - A network behavioral metric is extracted from a communication network based on a relevancy of the metric to network behavior by identifying a network metric x that is defined as a random variable that represents a quantitative measure of a network behavior accumulated over a period of time, selecting a network feature, generating a metric disintegration model for the network metric x comprising at least one normal behavior probability distribution function for the metric x for each value of the network feature, respectively, and at least one abnormal behavior probability distribution function for the metric x for each value of the network feature, respectively, increasing a number of the values of the metric x that indicates normal network behavior and/or abnormal network behavior based on the metric disintegration model, and selecting a network metric x as a behavioral metric based on a relevancy η of the network metric x to the network behavior. Embodiments for tracking network behavioral changes are also provided. | 04-23-2009 |
20090106175 | MANAGEMENT OF APPLICATIVE STREAMS IN MOBILE NETWORKS - A method is provided for constructing at least one decision graph for managing at least one applicative stream assigned to a terminal and set up between the terminal and a correspondent via at least one communication network. The method includes a step of dynamically constructing at least one possible decision graph for the one applicative stream assigned to the terminal, itself including a step of exchanging at least one configuration message between at least two decision modules pertaining to a predetermined set of decision modules. | 04-23-2009 |
20090112778 | Method and Apparatus for Leveraging End User Terminals in Self-Learning Networks - The invention includes a method and apparatus for configuring a self-learning network using feedback information received from an end user terminal communicating via the self-learning network. A method includes receiving feedback information from the end user terminal, generating configuration information for at least one network element of the self-learning network using the received feedback information, and configuring the at least one network element using the generated configuration information. The at least one network element of the self-learning network is configured by executing commands on each of the at least one network element and/or by propagating configuration information to each of the at least one network element. The feedback information may include user and/or terminal feedback information. The configuration information may include any information adapted for use in configuring the at least one network element of the self-learning network (and may also include configuration information for the end user terminal). | 04-30-2009 |
20090119234 | INTERACTIVE MACHINE LEARNING ADVICE FACILITY - In embodiments of the present invention improved capabilities are described for helping a user make a decision through the use of a machine learning facility. The process may begin with an initial question being received by the machine learning facility from the user. The user may then be provided with a dialog consisting of questions from the machine learning facility and answers provided by the user. The machine learning facility may then provide a decision to the user based on the dialog and pertaining to the initial question, such as a recommendation, a diagnosis, a conclusion, advice, and the like. In embodiments, future questions and decisions provided by the machine learning facility may be improved through feedback provided by the user. | 05-07-2009 |
20090119235 | SYSTEM AND METHOD FOR EXTRACTING ENTITIES OF INTEREST FROM TEXT USING N-GRAM MODELS - A document (or multiple documents) is analyzed to identify entities of interest within that document. This is accomplished by constructing n-gram or bi-gram models that correspond to different kinds of text entities, such as chemistry-related words and generic English words. The models can be constructed from training text selected to reflect a particular kind of text entity. The document is tokenized, and the tokens are run against the models to determine, for each token, which kind of text entity is most likely to be associated with that token. The entities of interest in the document can then be annotated accordingly. | 05-07-2009 |
20090125461 | Multi-Label Active Learning - Multi-label active learning may entail training a classifier with a set of training samples having multiple labels per sample. In an example embodiment, a method includes accepting a set of training samples, with the set of training samples having multiple respective samples that are each respectively associated with multiple labels. The set of training samples is analyzed to select a sample-label pair responsive to at least one error parameter. The selected sample-label pair is then submitted to an oracle for labeling. | 05-14-2009 |
20090125462 | Method and system using keyword vectors and associated metrics for learning and prediction of user correlation of targeted content messages in a mobile environment - Methods and systems for determining a suitability for a mobile client to display information are disclosed. A particular exemplary method includes receiving a plurality of sets of one or more first keywords on a mobile client, each set of first keywords associated with one or more respective first messages, monitoring user interaction of the respective first messages on the mobile client, performing learning operations on the mobile client with the first keywords based on monitored user interaction to estimate a set of keyword interest weights, receiving a set of target keywords associated with a target message, and displaying the target message on the mobile client based on the estimated keyword interest weights. | 05-14-2009 |
20090125463 | TECHNIQUE FOR CLASSIFYING DATA - Provided is a system that generates models for classifying input data into a plurality of classes on the basis of training data previously classified into the plurality of classes. The system includes a sampling unit and a learning unit. The sampling unit samples, from the training data, a plurality of datasets each including a predetermined number of elements classified into a minority class and a corresponding number of elements classified into a majority class, the corresponding number being determined in accordance with the predetermined number. The learning unit learns each of a plurality of models for classifying the input data into the plurality of classes, by using a machine learning technique on the basis of each of the plurality of sampled datasets. | 05-14-2009 |
20090132442 | Method and Apparatus for Determining Decision Points for Streaming Conversational Data - A method for determining a decision point in real-time for a data stream from a conversation includes receiving streaming conversational data; and determining when to classify the streaming conversational data, using a measure of certainty, by performing certainty calculations at a plurality of time instances during the conversation and by selecting a decision point in response to the certainty calculations, the decision point not being based on a fixed window of conversational data but being based on accumulated conversational data available at different ones of the plurality of time instances. Systems and computer program products are also provided. | 05-21-2009 |
20090132443 | Methods and Devices for Analyzing Lipoproteins - The disclosure describes methods, systems, and devices for analysis of lipoproteins and for diagnosing and/or determining risk of cardiovascular disease. In some embodiments, lipoproteins are separated by electrophoretically using a micro-channel device, and the data are analyzed using an adaptive method such as a neural network. | 05-21-2009 |
20090132444 | CONSTRAINED LINE SEARCH OPTIMIZATION FOR DISCRIMINATIVE TRAINING OF HMMS - An exemplary method for optimizing a continuous density hidden Markov model (CDHMM) includes imposing a constraint for discriminative training, approximating an objective function as a smooth function of CDHMM parameters and performing a constrained line search on the smoothed function to optimize values of the CDHMM parameters. Various other methods, devices and systems are disclosed. | 05-21-2009 |
20090132445 | GENERALIZED REDUCED ERROR LOGISTIC REGRESSION METHOD - A machine classification learning method titled Generalized Reduced Error Logistic Regression (RELR) is presented. The method overcomes significant limitations in prior art logistic regression and other machine classification learning methods. The method is applicable to all current applications of logistic regression, but has significantly greater accuracy using smaller sample sizes and larger numbers of input variables than other machine classification learning methods including prior art logistic regression. | 05-21-2009 |
20090132446 | Support Vector Machines Processing System - An implementation of SVM functionality improves efficiency, time consumption, and data security, reduces the parameter tuning challenges presented to the inexperienced user, and reduces the computational costs of building SVM models. A computer program product for support vector machine processing in a computer system comprises computer program instructions for storing data, providing an interface to client software, building a support vector machine model on at least a portion of the stored data, based on a plurality of model-building parameters, estimating values for at least some of the model-building parameters, and applying the support vector machine model using the stored data to generate a data mining output. | 05-21-2009 |
20090132447 | Support Vector Machines Processing System - An implementation of SVM functionality improves efficiency, time consumption, and data security, reduces the parameter tuning challenges presented to the inexperienced user, and reduces the computational costs of building SVM models. A system for support vector machine processing comprises data stored in the system, a client application programming interface operable to provide an interface to client software, a build unit operable to build a support vector machine model on at least a portion of the data stored in the system, the portion of the data selected using a stratified sampling method with respect to a target distribution, an apply unit operable to apply the support vector machine model using the data stored in the system. | 05-21-2009 |
20090144209 | SEQUENCE PREDICTION SYSTEM - The system includes a storage device | 06-04-2009 |
20090144210 | METHOD AND APPARATUS FOR DETERMINING THE VARIABLE DEPENDENCY - A method and an apparatus for determining variable dependency are disclosed. In the present invention, a variable dependency is determined in advance arbitrarily; partial variables are selected from the current variable dependency, and a legitimate superior variable set is re-selected for each of the partial variables, and the new variable dependency is stored only if it meets the criterion of acceptance; when the termination criterion for establishing variable dependency is met, the optimal variable dependency is determined from all variable dependencies. Because the existing variable dependency is not taken as a reference when the new variable dependency is created, the new variable dependency is not misled by the existing variable dependency, and the time for finding the globally optimal variable dependency can be shortened. | 06-04-2009 |
20090150308 | MAXIMUM ENTROPY MODEL PARAMETERIZATION - Described is a technology by which a maximum entropy model used for classification is trained with a significantly lesser amount of training data than is normally used in training other maximum entropy models, yet provides similar accuracy to the others. The maximum entropy model is initially parameterized with parameter values determined from weights obtained by training a vector space model or an n-gram model. The weights may be scaled into the initial parameter values by determining a scaling factor. Gaussian mean values may also be determined, and used for regularization in training the maximum entropy model. Scaling may also be applied to the Gaussian mean values. After initial parameterization, training comprises using training data to iteratively adjust the initial parameters into adjusted parameters until convergence is determined. | 06-11-2009 |
20090150309 | System and method for training a multi-class support vector machine to select a common subset of features for classifying objects - An improved system and method is provided for training a multi-class support vector machine to select a common subset of features for classifying objects. A multi-class support vector machine generator may be provided for learning classification functions to classify sets of objects into classes and may include a sparse support vector machine modeling engine for training a multi-class support vector machine using scaling factors by simultaneously selecting a common subset of features iteratively for all classes from sets of features representing each of the classes. An objective function using scaling factors to ensure sparsity of features may be iteratively minimized, and features may be retained and added until a small set of features stabilizes. Alternatively, a common subset of features may be found by iteratively removing at least one feature simultaneously for all classes from an active set of features initialized to represent the entire set of training features. | 06-11-2009 |
20090150310 | PORTABLE COMMUNICATION DEVICE, IN-VEHICLE COMMUNICATION DEVICE, AND COMMUNICATION SYSTEM - A communication system has: a portable communication device; and an in-vehicle communication device that communicates with the portable communication device through synchronous communication. The potable communication device transmits dummy signals simulating the vehicle information to the in-vehicle communication device in a first period. The in-vehicle communication device learns and records a first filter coefficient for filtering the dummy signals that are received in the first period and a second filter coefficient for filtering signals that are received from the sensors in a second period in which the portable communication device do not transmit the dummy signals, and the in-vehicle communication device calculates a third filter coefficient for removing noises on signals received from the sensors based on the first and second filter coefficients and then filters the received signals using the third filter coefficient. | 06-11-2009 |
20090150311 | Action based learning - A set of sequences of sensed input patterns associated with a set of actions is generated by performing at least a first action on data derived from a real-world system. A subset of the sequences of sensed input patterns that form a group associated with the first action is determined. A new sequence of sensed input patterns is received. A first value which indicates the probability that the new sequence of sensed input patterns is associated with the first action based on the subset of sequences of sensed input patterns is determined and stored in a memory associated with the computer system. | 06-11-2009 |
20090150312 | Systems And Methods For Analyzing Disparate Treatment In Financial Transactions - Systems and methods are provided for analyzing disparate treatment in financial transactions. Data processing software instructions may be used to process lending-related data to identify a plurality of primary factors and one or more secondary factors for use making a lending-related decision. Model facilitation software instructions may be used to receive one or more relationships between the primary factors and the one or more secondary factors, wherein the relationships define criteria in which one or more positive secondary factors will compensate for a negative primary factor in making the lending-related decision. Model generation software instructions may be used to analyze lending-related data based on the primary factors, secondary factors and the one or more relationships. | 06-11-2009 |
20090157571 | METHOD AND APPARATUS FOR MODEL-SHARED SUBSPACE BOOSTING FOR MULTI-LABEL CLASSIFICATION - A computer program product includes machine readable instructions for managing data items, the instructions stored on machine readable media, the product including instructions for: initializing a plurality of base models; minimizing a joint loss function to select models from the plurality for a plurality of labels associated with the data items; and at least one of sharing and combining the selected base models to formulate a composite classifier for each label. A computer system and additional computer program product are provided. | 06-18-2009 |
20090157572 | STACKED GENERALIZATION LEARNING FOR DOCUMENT ANNOTATION - A document annotation method includes modeling data elements of an input document and dependencies between the data elements as a dependency network. Static features of at least some of the data elements are defined, each expressing a relationship between a characteristic of the data element and its label. Dynamic features are defined which define links between an element and labels of the element and of a second element. Parameters of a collective probabilistic model for the document are learned, each expressing a conditional probability that a first data element should be labeled with information derived from a label of a neighbor data element linked to the first data element by a dynamic feature. The learning includes decomposing a globally trained model into a set of local learning models. The local learning models each employ static features to generate estimations of the neighbor element labels for at least one of the data elements. | 06-18-2009 |
20090157573 | System And Method For Grading Electricity Distribution Network Feeders Susceptible To Impending Failure - A machine learning system creates failure-susceptibility rankings for feeder cables in a utility's electrical distribution system. The machine learning system employs martingale boosting algorithms and Support Vector Machine (SVM) algorithms to generate a feeder failure prediction model, which is trained on static and dynamic feeder attribute data. Feeders are dynamically ranked by failure susceptibility and the rankings displayed to utility operators and engineers so that they can proactively service the distribution system to prevent local power outages. The feeder rankings may be used to redirect power flows and to prioritize repairs. A feedback loop is established to evaluate the responses of the electrical distribution system to field actions taken to optimize preventive maintenance programs. | 06-18-2009 |
20090157574 | METHOD AND APPARATUS FOR ANALYZING WEB SERVER LOG BY INTRUSION DETECTION SYSTEM - Provided is hacking prevention technology, and more particularly, a method and apparatus for automatically analyzing log information of a web server for which intrusion is attempted from an outside source. | 06-18-2009 |
20090164394 | AUTOMATED CREATIVE ASSISTANCE - The claimed subject matter provides a system and/or a method that facilitates generating a suggestion for a creative work. An interface component can receive a portion of a creative work. A muse component can evaluate the portion of the creative work utilizing a machine learning technique, wherein the muse component can identify a portion of suggested content based upon the evaluation. A palette can be populated with the portion of suggested content, wherein the portion of suggested content is a subset of content that is available for incorporation into the creative work. | 06-25-2009 |
20090171866 | System and method for learning associations between logical objects and determining relevance based upon user activity - User activity streams are used to automatically learn associations between logical objects and form logical groups. Search results are sorted based upon their relevance to current user activity. Combined with a graphical user interface component and object database, the invention automatically retrieves and display groups and objects related to the active object. | 07-02-2009 |
20090171867 | DETERMINING QUALITY OF TIER ASSIGNMENTS - Described herein is a method that includes receiving user history data and generating an indication of quality of a tier assignment used to store searchable digital items in a tiered storage system, wherein the indication is based at least in part upon a subset of the user history data. Also described herein is a system that includes a receiver component that receives user history data. The system further includes a quality indicator component that determines an indication of quality of a tier assignment used to store digital items that are retrievable by way of querying, wherein the quality indicator component generates the indication based at least in part upon a subset of the user history data and the tier assignment indicates where digital items are to be stored in a tiered storage system. | 07-02-2009 |
20090171868 | Method and Apparatus for Early Termination in Training of Support Vector Machines - Disclosed is a method for early termination in training support vector machines. A support vector machine is iteratively trained based on training examples using an objective function having primal and dual formulations. At each iteration, a termination threshold is calculated based on the current SVM solution. The termination threshold increases with the number of training examples. The termination threshold can be calculated based on the observed variance of the loss for the current SVM solution. The termination threshold is compared to a duality gap between primal and dual formulations at the current SVM solution. When the duality gap is less than the termination threshold, the training is terminated. | 07-02-2009 |
20090171869 | HOT TERM PREDICTION FOR CONTEXTUAL SHORTCUTS - Subject matter disclosed herein may relate to predicting hot terms, and may also relate to creating contextual shortcuts based, at least in part, on the predicted hot terms. | 07-02-2009 |
20090171870 | System and method of feature selection for text classification using subspace sampling - An improved system and method is provided for feature selection for text classification using subspace sampling. A text classifier generator may be provided for selecting a small set of features using subspace sampling from the corpus of training data to train a text classifier for using the small set of features for classification of texts. To select the small set of features, a subspace of features from the corpus of training data may be randomly sampled according to a probability distribution over the set of features where a probability may be assigned to each of the features that is proportional to the square of the Euclidean norms of the rows of left singular vectors of a matrix of the features representing the corpus of training texts. The small set of features may classify texts using only the relevant features among a very large number of training features. | 07-02-2009 |
20090171871 | COMBINATION MACHINE LEARNING ALGORITHMS FOR COMPUTER-AIDED DETECTION, REVIEW AND DIAGNOSIS - This invention utilizes a number of Computational Intelligence (CI) techniques with different learning methods in a computer-aided detection, review and diagnosis (CAD) device. Specifically, an unsupervised learning method is used for clustering of types of abnormal findings. Then a number of classifiers for each type of findings are trained with appropriate learning algorithms; and combined in three different manners to produce one classifier that can be operated at three different operating points. A fuzzy system is used for mapping the findings to diagnostic reports constructed using a formal language. Finally, the finding statistics is calculated based on Bayesian probability. During image review, the device provides the readers some insight as to how it derives its outputs. The output of the device can be updated in an interactive and progressive manner by a human reader (radiologist). The output from classification can be updated by the human, and is fed as input to the assessment task. Again the output from assessment can be updated by the human reader, and is fed as input for the machine to produce statistical information. If so configured, the interactive information can be added to an online database so that the device can adapt its future behavior based on the new information. | 07-02-2009 |
20090171872 | Selection of head and neck cancer patients for treatment with drugs targeting EGFR pathway - Methods using mass spectral data analysis and a classification algorithm provide an ability to determine whether a head and neck squamous cell carcinoma (HNSCC) patient is likely to benefit from a drug targeting an epidermal growth factor receptor pathway, including small molecule epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) and monoclonal antibody EGFR inhibitors. | 07-02-2009 |
20090177597 | SYSTEMS, METHODS AND COMPUTER PRODUCTS FOR PROFILE BASED IDENTITY VERIFICATION OVER THE INTERNET - Systems, methods and computer products for profile-based identity verification over the Internet. Exemplary embodiments include a system including an activity classifier configured to receive Internet activity input including email, chat, browser and voice over Internet Protocol (VoIP) logs/streams, an email profiler, a chat, a browser profiler, a voice over Internet Protocol (VoIP) logs/streams profiler, wherein the profilers are configured to extract values from the Internet Activity input attributes from the data set, a score calculator configured to receive the attributes and calculate the score of the data set, a categorization engine configured to receive the score from the score calculator and map the data set to an individual or class of individuals based on the value of the score and on a database of activity-specific attributes and an application configured to place weights on the activity specific and generic attributes to define a score function from the score. | 07-09-2009 |
20090182689 | RULE-BASED DYNAMIC OPERATION EVALUATION - A computer program may involve a dynamic operation, which may specify one of many types of methods based on the conditions of the invocation during runtime, such as the parameters provided to the dynamic operation. The appropriate performance of the dynamic method may be achieved by analyzing the conditions of the invocation according to an evaluation rule set, the rules comprising conditions and an action to be performed if the conditions are satisfied. The evaluation rule set may also be reconfigured upon identifying a satisfied rule to facilitate a faster evaluation of the dynamic operation during a second and subsequent invocations. | 07-16-2009 |
20090182690 | Detection and Classification of Light Sources Using a Diffraction Grating - A system mounted in a vehicle for classifying light sources. The system includes a lens and a spatial image sensor. The lens is adapted to provide an image of a light source on the spatial image sensor. A diffraction grating is disposed between the lens and the light source. The diffraction grating is adapted for providing a spectrum. A processor is configured for classifying the light source as belonging to a class selected from a plurality of classes of light sources expected to be found in the vicinity of the vehicle, wherein the spectrum is used for the classifying of the light source. Both the image and the spectrum may be used for classifying the light source or the spectrum is used for classifying the light source and the image is used for another driver assistance application. | 07-16-2009 |
20090182691 | METHOD AND SYSTEM OF ENHANCING GANGLION CELL FUNCTION TO IMPROVE PHYSICAL PERFORMANCE - A method and a system of enhancing ganglion cell function using a gaming environment corresponding to a physical activity. The method and system may be used to implement one or more processes to improve a person's visual processing profile. In particular, the method and system may be used to improve a player's skill in the corresponding physical activity. | 07-16-2009 |
20090187515 | QUERY SUGGESTION GENERATION - Described herein is a system that facilitates assigning indications of usefulness to query suggestions. The system includes a query suggestion generator component that receives a query and generates a query suggestion based at least in part upon the received query. A model component outputs an indication of usefulness with respect to the query suggestion, wherein the model component is a machine-learned model of user behavior with respect to query suggestions. | 07-23-2009 |
20090187516 | SEARCH SUMMARY RESULT EVALUATION MODEL METHODS AND SYSTEMS - Methods and systems are provided herein for establishing and/or using an evaluation model that is adapted to determine a model judgment value based, at least in part, on measured summary feature values associated with a search result summary. The evaluation model may be established through a learning process based, at least in part, on human judgment values associated with a set of search result summaries. | 07-23-2009 |
20090187517 | MODIFICATION OF RELATIONAL MODELS - Described herein is a system that facilitates modifying a relational model. The system includes a first model component that is a relational model that includes a plurality of atoms. The system further includes a modifier component that automatically assigns values to a plurality of atoms in the relational model by clustering atoms of the relational model to create a second model component, wherein the second model component is a relational model. | 07-23-2009 |
20090187518 | AUTOMATICALLY IDENTIFYING AN OPTIMAL SET OF ATTRIBUTES TO FACILITATE GENERATING BEST PRACTICES FOR CONFIGURING A NETWORKED SYSTEM - A method and system for automatically identifying an optimal set of attributes of entities included in a networked system. Entity types are ranked based on information gain. A first classification accuracy relative to a first entity type is determined. The first entity type is the top-ranked entity type or a first aggregate entity type. A second entity type is selected base on the ranking. A database join of a first set of attributes associated with the first entity type and a second set of attributes associated with the second entity type is performed. A second classification accuracy relative to a second aggregate entity type generated by the join is determined. In response to determining that the second classification accuracy is not greater than the first classification accuracy, an optimal set of attributes contributing to a problem in the networked system is identified as the first set of attributes. | 07-23-2009 |
20090187519 | Learning Device Interaction Rules - Devices and methods are disclosed for establishing interaction among electronic devices of an environment. The device has a transmitter, receiver, memory for storing interaction rules, and a processor for learning the interaction rules in association with the transmitter, receiver, and other devices of the environment. The device also includes components for performing the device specific functions and a state sensor for determining the logical or physical state of the device. Methods involve observing at one or more devices change of state activity among the plurality of devices through receiving a change of state message that is transmitted to the one or more devices. A set of rules are learned at the one or more devices based upon observing the change of state activity. The learned set of rules are then applied at the one or more devices to automatically control changes of state of devices within the plurality of devices. | 07-23-2009 |
20090192955 | GRANULAR SUPPORT VECTOR MACHINE WITH RANDOM GRANULARITY - Methods and systems for granular support vector machines. Granular support vector machines can randomly select samples of datapoints and project the samples of datapoints into a randomly selected subspaces to derive granules. A support vector machine can then be used to identify hyperplane classifiers respectively associated with the granules. The hyperplane classifiers can be used on an unknown datapoint to provide a plurality of predictions which can be aggregated to provide a final prediction associated with the datapoint. | 07-30-2009 |
20090192956 | METHOD AND APPARATUS FOR STRUCTURING DOCUMENTS UTILIZING RECOGNITION OF AN ORDERED SEQUENCE OF IDENTIFIERS - A method is provided for operating a computing device to create a document structure model of a computer parsable text document utilizing recognition of at least one ordered sequence of identifiers in the document. The method includes converting a computer parsable text document of any format to an alternative structured language format to form a converted document. The text of the converted document is fragmented into an ordered sequence of text fragments within a text format. The text fragments are enumerated to obtain a sequence of terms. At least one optimal sub-sequence of terms is identified from among the sequence of terms, with an optimal sub-sequence being one or more longest increasing sub-sequence(s). The computer parsable text document is annotated with tags, with the tags including information derived from identification of the optimal sub-sequence(s). The annotated document is displayed on the graphical user interface. | 07-30-2009 |
20090198635 | OPTICAL METROLOGY OF STRUCTURES FORMED ON SEMICONDUCTOR WAFERS USING MACHINE LEARNING SYSTEMS - A structure formed on a semiconductor wafer is examined by obtaining a first diffraction signal measured using a metrology device. A second diffraction signal is generated using a machine learning system, where the machine learning system receives as an input one or more parameters that characterize a profile of the structure to generate the second diffraction signal. The first and second diffraction signals are compared. When the first and second diffraction signals match within a matching criterion, a feature of the structure is determined based on the one or more parameters or the profile used by the machine learning system to generate the second diffraction signal. | 08-06-2009 |
20090204553 | Feature Reduction Method for Decision Machines - A method for feature reduction in a training set for a learning machine such as a Support Vector Machine (SVM). In one embodiment the method includes a step ( | 08-13-2009 |
20090204554 | Direction-aware proximity for graph mining - A method and system for graph mining direction-aware proximity measurements. A directed graph includes nodes and directed edges connecting the nodes. A direction-aware proximity measurement is calculated from a first node to a second node or from a first group of nodes to a second group of nodes. The direction-aware proximity measurement from a first node to second node is based on an escape probability from the first node to the second node. Disclosed herein are methods for efficiently calculating one or multiple direction-aware proximity measurements. The direction-aware proximity measurements can be used in performing various graph mining applications. | 08-13-2009 |
20090204555 | SYSTEM AND METHOD USING HIDDEN INFORMATION - A method and system for use in describing a phenomenon of interest. The method and system computes a decision rule for use in describing the phenomenon of interest using training data relating to the phenomenon of interest, labels for labeling the training data, and hidden information about the training data or directed distances obtained from the hidden information, as inputs. | 08-13-2009 |
20090204556 | Large Scale Manifold Transduction - A method for training a learning machine for use in discriminative classification and regression includes randomly selecting, in a first computer process, an unclassified datapoint associated with a phenomenon of interest; determining, in a second computer process, a set of datapoints associated with the phenomenon of interest that is likely to be in the same class as the selected unclassified datapoint; predicting, in a third computer process, a class label for the selected unclassified datapoint in a third computer process; predicting a class label for the set of datapoints in a fourth computer process; combining the predicted class labels in a fifth computer process, to predict a composite class label that describes the selected unclassified datapoint and the set of datapoints; and using the combined class label to adjust at least one parameter of the learning machine in a sixth computer process. | 08-13-2009 |
20090204557 | Method and System for Analysis of Flow Cytometry Data Using Support Vector Machines - An automated method and system are provided for receiving an input of flow cytometry data and analyzing the data using one or more support vector machines to generate an output in which the flow cytometry data is classified into two or more categories. The one or more support vector machines utilizes a kernel that captures distributional data within the input data. Such a distributional kernel is constructed by using a distance function (divergence) between two distributions. In the preferred embodiment, a kernel based upon the Bhattacharya affinity is used. The distributional kernel is applied to classification of flow cytometry data obtained from patients suspected having myelodysplastic syndrome. | 08-13-2009 |
20090210362 | OBJECT DETECTOR TRAINED USING A WORKING SET OF TRAINING DATA - An object detector that includes a number of weak classifiers can be trained using a subset (a “working set”) of training data instead of all of the training data. The working set can be updated so that, for example, it remains representative of the training data. A decision to update the working set may be made based on the false positive sample rate—if that rate falls below a threshold value, an update of the working set can be triggered. | 08-20-2009 |
20090210363 | SYSTEMS, METHODS AND COMPUTER PROGRAM PRODUCTS FOR SUPERVISED DIMENSIONALITY REDUCTION WITH MIXED-TYPE FEATURES AND LABELS - Systems, methods and computer program products for supervised dimensionality reduction. Exemplary embodiments include a method including receiving an input in the form of a data matrix X of size N×D, wherein N is a number of samples, D is a dimensionality, a vector Y of size N×1, hidden variables U of a number K, a data type of the matrix X and the vector Y, and a trade-off constant alpha; selecting loss functions in the form of Lx(X,UV) and Ly(Y,UW) appropriate for the type of data in the matrix X and the vector Y, where U, V and W are matrices, selecting corresponding sets of update rules RU, RV and RW for updating the matrices U,V and W, learning U, V and W that provide a minimum total loss L(U,V,W)=Lx(X,UV)+alpha*Ly(Y,UW), and returning matrices U, V and W. | 08-20-2009 |
20090210364 | Apparatus for and Method of Generating Complex Event Processing System Rules - A novel and useful mechanism enabling a standard learning algorithm to generate rules for complex event processing (CEP) systems. The method creates rules that infer previously defined output events by creating input event feature vectors for each targeted output event. In addition, a method for automatically generating CEP system rules to infer output events which are anomalies (i.e. statistical outliers) of input event sequences is disclosed. Input feature vectors consisting of multiple input events and parameters for each targeted output event are then input into a standard learning algorithm to generate CEP system rules. | 08-20-2009 |
20090210365 | System and method for combining hetergeneous predictors with an application to survival anaylsis - A method, a system, and a computer-readable medium for predicting a risk in a survival analysis for a plurality of individuals characterized by at least one predictor are disclosed. A method for estimating risk order of an individual, given information about a set of individuals, characterized by one or many predictors, and provided that direction of association between each predictor and the risk order is known, comprising the step of comparing the individual with each individual within the set of individuals, and estimating risk of individual based on set comparisons. | 08-20-2009 |
20090216692 | Information Processing Apparatus and Method, and Program - The invention relates to an information processing apparatus and method and a program which can provide an item suitable for a feeling (mood) of a user. A user data acquisition section | 08-27-2009 |
20090216693 | CLASSIFICATION METHOD AND APPARATUS - A method for building a classification model for classifying unclassified documents based on the classification of a plurality of documents which respectively have been classified as belonging to one of a plurality of classes, said documents being digitally represented in a computer, said documents respectively comprising a plurality of terms which respectively comprise one or more symbols of a finite set of symbols, and said method comprising the following steps: representing each of said plurality of documents by a vector of n dimensions, said n dimensions forming a vector spaces whereas the value of each dimension of said vector corresponds to the frequency of occurrence of a certain term in the document corresponding to said vector, so that said n dimensions span up a vector space; representing the classification of said already classified documents into classes by separating said vector space into a plurality of subspaces by one or more hyperplanes, such that each subspace comprises one or more documents as represented by their corresponding vectors in said vector space, so that said each subspace corresponds to a class. | 08-27-2009 |
20090216694 | MAXIMIZATION OF SUSTAINED THROUGHPUT OF DISTRIBUTED CONTINUOUS QUERIES - A system, method, and computer readable medium for optimizing throughput of a stream processing system are disclosed. The method comprises analyzing a set of input streams and creating, based on the analyzing, an input profile for at least one input stream in the set of input streams. The input profile comprises at least a set of processing requirements associated with the input stream. The method also comprises generating a search space, based on an initial configuration, comprising a plurality of configurations associated with the input stream. A configuration in the plurality of configurations is identified that increases throughput more than the other configurations in the plurality of configurations based on at least one of the input profile and system resources. | 08-27-2009 |
20090222387 | Diagnosis, Prognosis and Prediction of Recurrence of Breat Cancer - The present invention relates to methods and compositions for the diagnosis, prognosis, and prediction of breast cancer. More specifically, the invention relates to classification of breast cancer tissue samples based on measuring the expression of a set of marker genes. The set is useful for the identification of clinically important breast cancer subtypes. Methods are disclosed for prediction, diagnosis and prognosis of breast cancer. | 09-03-2009 |
20090222388 | Method of and system for hierarchical human/crowd behavior detection - The present invention is directed to a computer automated method of selectively identifying a user specified behavior of a crowd. The method comprises receiving video data but can also include audio data and sensor data. The video data contains images a crowd. The video data is processed to extract hierarchical human and crowd features. The detected crowd features are processed to detect a selectable crowd behavior. The selected crowd behavior detected is specified by a configurable behavior rule. Human detection is provided by a hybrid human detector algorithm which can include Adaboost or convolutional neural network. Crowd features are detected using textual analysis techniques. The configurable crowd behavior for detection can be defined by crowd behavioral language. | 09-03-2009 |
20090222389 | CHANGE ANALYSIS SYSTEM, METHOD AND PROGRAM - Different virtual labels, for example, like +1 and −1, are assigned to two data sets. A change analysis problem for the two data sets is reduced to a supervised learning problem by using the virtual labels. Specifically, a classifier such as logical regression, decision tree and SVM is prepared and is trained by use of a data set obtained by merging the two data sets assigned the virtual labels. A feature selection function of the resultant classifier is used to rank and output both every attribute contributing to classification and its contribution rate. | 09-03-2009 |
20090234782 | METHOD AND APPARATUS FOR LOCATION EVALUATION AND SITE SELECTION - Method, apparatus and system for location evaluation and site selection, capable of effectively configuring the site network and evaluating the facility location by scientifically modeling and incorporating human knowledge are provided. In one aspect, geographic and demographic data associated with a plurality of locations and human knowledge comprising partial rating knowledge and pair-wise preference knowledge are used in a regression algorithm to construct a location evaluation model. The regression algorithm is further refined using active learning that identifies a plurality of pairs of locations to improve precision of the regression algorithm. | 09-17-2009 |
20090234783 | VALUE FUNCTION REPRESENTATION METHOD OF REINFORCEMENT LEARNING AND APPARATUS USING THIS - Reinforcement learning is one of the intellectual operations applied to autonomously moving robots etc. It is a system having excellent sides, for example, enabling operation in unknown environments. However, it has the basic problem called the “incomplete perception problem”. A variety of solution has been proposed, but none has been decisive. The systems also become complex. A simple and effective method of solution has been desired. | 09-17-2009 |
20090234784 | Method of Providing Selected Content Items to a User - A method for providing selected content items to a user. The selection of content items is based on metadata pre-assigned to content items, typically authored content metadata, and on metadata generated and associated afterwards, called derived content metadata. Additionally, the selection of content items can be based also on context metadata, particularly derived context metadata. Derived metadata are automatically generated on the basis of derivation rules corresponding to algorithms to be applied to, e.g., the content of content items, authored content metadata and context metadata. User profiles can be used for improving the selection quality. A method is also disclosed for building and maintaining user profiles based on machine learning techniques. | 09-17-2009 |
20090240636 | Method, computer program with program code means and computer program product for analyzing variables influencing a combustion process in a combustion chamber, using a trainable statistical model - The invention relates to sensitivity analysis of variables influencing a combustion process. A trainable, statistical model is trained in such a way that it describes the combustion process in the combustion chamber. The trained statistical model is used to determine the influence of the variables on said combustion process in the combustion chamber. | 09-24-2009 |
20090240637 | RESILIENT CLASSIFIER FOR RULE-BASED SYSTEM - A resilient classifier for using with a rule-based system is provided. A system for classifying data for a rule-based system, may include: a system(s) for generating two training data sets, one data set is generated from input data while the second data set is generated from disturbed data; a system for merging the two training data sets; and a system for training a data classifier with the merged training data sets. As a result, the classification of data becomes more accurate, including when disturbed data is encountered. | 09-24-2009 |
20090240638 | SYNTACTIC AND/OR SEMANTIC ANALYSIS OF UNIFORM RESOURCE IDENTIFIERS - Subject matter disclosed herein may relate to analyses of uniform resource identifiers associated with web pages, and further may relate to gathering information about web pages by analyzing the uniform resource identifiers. | 09-24-2009 |
20090240639 | Feedback in Group Based Hierarchical Temporal Memory System - A Hierarchical Temporal Memory (HTM) network has at least first nodes and a second node at a higher level than the first nodes. The second node provides an inter-node feedback signal to the first nodes for grouping patterns and sequences (or co-occurrences) in input data received at the first nodes at the first nodes. The second node collects forward signals from the first nodes; and thus, the second node has information about the grouping of the patterns and sequences (or co-occurrences) at the first nodes. The second node provides inter-node feedback signals to the first nodes based on which the first nodes may perform the grouping of the patterns and sequences (or co-occurrences) at the first nodes. Also, a node in a Hierarchical Temporal Memory (HTM) network comprising a co-occurrence detector and a group learner coupled to the co-occurrence detector. The group learner provides an intra-node feedback signal to the co-occurrence detector including information on the grouping of the co-occurrences. The co-occurrence detector may select co-occurrences to be split, merged, retained or discarded based on the intra-node feedback signals. | 09-24-2009 |
20090240640 | Apparatus and method for predicting engine test performance from bench test data - An apparatus for evaluating deposit formation characteristics of lubricant samples. The apparatus includes a reactor chamber having a closed first end and a second end, said closed first end of said reactor chamber forming a lubricant sump and said second end open to the atmosphere, an electric heater for heating a test coupon positioned thereon, said electric heater positioned within said reactor chamber, above said lubricant sump, an air supply tube, said air supply tube having a first end in fluid communication with a source of air and a second end, said second end positioned adjacent the test coupon, said air supply tube having a sample orifice located between said first end and said second end of said air supply tube and a lubricant supply tube, said lubricant supply tube having a first end positioned within said lubricant sump and in fluid communication with a source of lubricant and a second end, said second end positioned within said sample orifice of said air supply tube. A method of predicting lubricant deposit formation in an end use engine test and a method for predicting whether a candidate lubricant sample will pass an End Use Test are also provided. | 09-24-2009 |
20090248595 | NAME VERIFICATION USING MACHINE LEARNING - Computer-enabled methods, apparatus, and computer-readable media are provided for verifying that a given network name, such as a URL, is an official, e.g., registered, approved, or otherwise officially recognized, network name that refers to or identifies a principal, such as a business. These techniques involve receiving a principal name and a given network name, receiving at least one feature attribute from at least one database of feature attributes, wherein the at least one feature attribute comprises a characteristic of the principal name or a characteristic of the network name, and invoking a logistic regression method to generate a probability, based upon the at least one feature attribute, that the given network name is an official network name for the principal name. The logistic regression method may include a gradient boosting tree model that generates the probability based upon the at least one feature attribute. | 10-01-2009 |
20090248596 | CONFIGURATION INFORMATION MANAGEMENT APPARATUS, CONFIGURATION INFORMATION MANAGEMENT PROGRAM, AND CONFIGURATION INFORMATION MANAGEMENT METHOD - A CMDB (configuration information management database) stores a CI (configuration item) and know-how separately. A CMDB data update management unit associates with each set of “property:value” stored in the CI with related know-how in the CMDB. The know-how stores a set of “property:value” common to a number of associated CIs. | 10-01-2009 |
20090254496 | SYSTEM AND METHOD FOR OPTIMIZING PATTERN RECOGNITION OF NON-GAUSSIAN PARAMETERS - A method of optimizing a function of a parameter includes associating, with an objective function for initial value of parameters, an auxiliary function of parameters that could be optimized computationally more efficiently than an original objective function, obtaining parameters that are optimum for the auxiliary function, obtaining updated parameters by taking a weighted sum of the optimum of the auxiliary function and initial model parameters. | 10-08-2009 |
20090254497 | METHOD AND SYSTEM FOR DYNAMIC PERFORMANCE MODELING OF COMPUTER APPLICATION SERVICES - A generic queueing network model of a Web services environment is introduced. The behavior of a service is abstracted in three phases: serial, parallel and dormant, thus yielding a Serial Parallel Queueing Network (SPQN) model with a small number of parameters. A method is provided for estimated the parameters of the model that is based on stochastic approximation techniques for solving stochastic optimization problems. The parameter estimation method is shown to perform well in a noisy environment, where performance data is obtained through measurements or using approximate model simulations. | 10-08-2009 |
20090254498 | System and method for identifying critical emails - Disclosed is a method and system for identifying critical emails. To identify critical emails, a critical email classifier is trained from training data comprising labeled emails. The classifier extracts N-grams from the training data and identifies N-gram features from the extracted N-grams. The classifier also extracts salient features from the training data. The classifier is trained based on the identified N-gram features and the salient features so that the classifier can classify unlabeled emails as critical emails or non-critical emails. | 10-08-2009 |
20090254499 | TECHNIQUES TO FILTER MEDIA CONTENT BASED ON ENTITY REPUTATION - Techniques to filter media content based on entity reputation are described. An apparatus may comprise a reputation subsystem operative to manage an entity reputation score for an entity. The reputation subsystem comprising a reputation manager component and a reputation input/output (I/O) component. The reputation manager component may comprise, among other elements, a data collection module operative to collect reputation information for an entity from a selected set of multiple reputation sources. The reputation manager component may also comprise a feature manager module communicatively coupled to the data collection module, the feature manager module operative to extract a selected set of reputation features from the reputation information. The reputation manager component may further comprise a reputation scoring module communicatively coupled to the feature manager module, the reputation scoring module operative to generate an entity reputation score based on the reputation features using a supervised or unsupervised machine learning algorithm. Other embodiments are described and claimed. | 10-08-2009 |
20090254500 | CONTROL SYSTEM FOR NETWORK OF INPUT DEVICES WITH AUTOMATIC AUDIO/VIDEO RECEIVER DETECTION AND CONTROL CONFIGURATION - Apparatus, methods, and systems for centrally and uniformly controlling the operation of a variety of devices, such as communication, consumer electronic, audio-video, analog, digital, 1394, and the like, over a variety of protocols within a network system and, more particularly, a control system and uniform user interface for centrally controlling these devices in a manner that appears seamless and transparent to the user. In a one embodiment, the control system will detect the change of state of an audio output sensor coupled to the audio output port. | 10-08-2009 |
20090254501 | WORD-SPACING CORRECTION SYSTEM AND METHOD - A word-spacing correction system and method are provided to automatically recognize and correct errors in the spacing of word inputs in an electronic device with relatively low computing power. In a learning process, probability information about each feature is created from a corpus of correct words, and then error correction rules are created by applying the probability information to a corpus of incorrect words from which all spaces between words of the corpus of correct words are removed. In an applying process, word-spacing in a user's input sentence is corrected by applying the probability information and the error correction rules to the user's input sentence. | 10-08-2009 |
20090259604 | METHODS, COMPUTER DEVICES, AND COMPUTER PROGRAM PRODUCTS FOR REGRESSION FROM INTERVAL TARGET VALUES - Methods, computing devices, and computer program products for regression from interval target values are provided. Training data having an interval output are read. An initial model is estimated. Representative values for the interval output are assigned using the initial model. A regression model is estimated using the representative values for the interval output. A determination is made whether the regression model converges. The step of assigning representative values for the interval output is iterated and the step of estimating the regression model using the representative values for the interval output iterated, in response to the regression model not converging. In response to the regression model converging, the regression model is output. | 10-15-2009 |
20090265290 | OPTIMIZING RANKING FUNCTIONS USING CLICK DATA - A system for optimizing machine-learned ranking functions based on click data. The system determines the weighting for each feature of a plurality of features according to a learning model based on the click data. The system selects an element from a plurality of elements for display on a web page based on the weighting of each feature of the plurality of features. The system may rank the items to form a list on the web page based on the weighted features in order of inferred relevance according to the online learning model. | 10-22-2009 |
20090265291 | Information Processing Device and Method, and Program - An information processing device includes: a candidate generating unit employing a user evaluation matrix of evaluation values indicating evaluations as to multiple contents for multiple users to generate multiple estimated expression candidates which are candidates of an estimated expression employed for estimating an evaluation as to a content of a user; an estimation results computing unit computing the user evaluation matrix by the respective estimated expression candidates to generate an estimation result configured of a predictive evaluation value which is the estimation value of an evaluation value; and an estimated expression selecting unit, in a case where several estimation results are employed, and several estimated expression candidates are employed as estimated expressions, obtaining linear combination coefficients employed for obtaining a final estimation result, and selecting an estimated expression candidate and linear combination coefficient having the highest evaluation as the estimated expression and linear combination coefficient of the next generation. | 10-22-2009 |
20090271338 | SCALABLE FEATURE SELECTION FOR MULTI-CLASS PROBLEMS - In a feature filtering approach, a set of relevant features and a set of training objects classified respective to a set of classes are provided. A candidate feature and a second feature are selected from the set of relevant features. An approximate Markov blanket criterion is computed that is indicative of whether the candidate feature is redundant in view of the second feature. The approximate Markov blanket criterion includes at least one dependency on less than the entire set of classes. An optimized set of relevant features is defined, consisting of a sub-set of the set of relevant features from which features indicated as redundant by the selecting and computing are removed. | 10-29-2009 |
20090271339 | Hierarchical Recognition Through Semantic Embedding - Computer-implemented systems and methods, including servers, perform structure-based recognition processes that include matching and classification. Preprocessing subsystems and sub-methods embed a set of classes on which a loss function is defined into a semantic space and learn an input mapping between an input space and the semantic space. Recognition subsystems and methods accept a test object, representable in the input space, and apply the input mapping to the test object as part of a recognition process. | 10-29-2009 |
20090271340 | Method for the computer-aided learning of a control or adjustment of a technical system - A method for the computer-aided learning of a control of a technical system is provided. An operation of the technical system is characterized by states which the technical system can assume during operation. Actions are executed during the operation and convert a relevant state into a subsequent state. The method is characterized in that, when learning the control, suitable consideration is given to the statistical uncertainty of the training data. This is achieved in that the statistical uncertainty of a quality function which models an optimal operation of the technical system is specified by an uncertainty propagation and is incorporated into an action selection rule when learning. By a correspondingly selectable certainty parameter, the learning method can be adapted to different application scenarios which vary in statistical requirements. The method can be used for learning the control of an operation of a turbine, in particular a gas turbine. | 10-29-2009 |
20090276377 | NETWORK DATA MINING TO DETERMINE USER INTEREST - Mining information from network data traffic to determine interests of online network users is provided herein. A data packet received at a network interface device can be accessed and inspected at line rate speeds. Source or addressing information in the data packet can be extracted to identify an initiating and/or receiving device. The packet can be inspected to identify occurrences of keywords or data features related with one or more subject matters. A vector can be defined for a network device that indicates a relative rank of interest in various subject matters. Furthermore, statistical analysis can be implemented on data stored in one or more interest vectors to determine information pertinent to network user interests. The information can facilitate providing value-added products or services to network users. | 11-05-2009 |
20090276378 | System and Method for Identifying Document Structure and Associated Metainformation and Facilitating Appropriate Processing - A system and method for processing documents by utilizing the textual content and layout of the documents, including visual indicators, to more efficiently and reliably process the documents across various document types. The system and method identifies visually distinguishable elements within the document, such as section and sub-section boundary indicators, to mark, divide and label the boundaries and content type such that the sections are more clearly identifiable and easily processed. The system and method uses known elements, including section heading types, keywords, section type classifiers, sub-section heading constructs, stop words, and the like to adaptively identify and process a broad range of document types. The system and method continually refines and updates these known elements and allows users to discover and define new elements for further refinement and updating. | 11-05-2009 |
20090276379 | Using automatically generated decision trees to assist in the process of design and review documentation - An embodiment of this invention is to use automatically generated decision trees to assist in the design and review process. In one embodiment, the decision trees are automatically extracted from data describing a system (in case of design process) or a review artifact (in case of review process). In a further embodiment, the decision trees are then used in the design process, and the order of attributes in the decision tree suggests a new order for writing the design document. | 11-05-2009 |
20090276380 | COMPUTER-AIDED NATURAL LANGUAGE ANNOTATION - The present invention uses a natural language understanding system that is currently being trained to assist in annotating training data for training that natural language understanding system. Unannotated training data is provided to the system and the system proposes annotations to the training data. The user is offered an opportunity to confirm or correct the proposed annotations, and the system is trained with the corrected or verified annotations. | 11-05-2009 |
20090276381 | SOCIAL KNOWLEDGE SYSTEM CONTENT QUALITY - Techniques for automatically scoring submissions to an online question-and-answer submission system are disclosed. According to one such technique, an initial set of user submissions are scored by human operators and/or automated algorithmic mechanisms. The submissions and their accompanying scores are provided as training data to an automated machine learning mechanism. The machine learning mechanism processes the training data and automatically detects patterns in the provided submissions. The machine learning mechanism automatically correlates these patterns with the scores assigned to the submissions that match those patterns. As a result, the machine learning mechanism is trained. Thereafter, the machine learning mechanism processes unscored submissions. The machine learning mechanism automatically identifies, from among the patterns that the machine learning mechanism has already detected, one or more patterns that these submissions match. The machine learning mechanism automatically scores these submissions based on the matching patterns and the scores that are associated with those patterns. | 11-05-2009 |
20090276382 | DETECTION OF UNKNOWN SCENARIOS - The present invention provides methods, systems and apparatus for detecting unknown scenarios in a data processing system. An example method includes the steps of: providing known scenario data describing one or more known scenarios in a database; generating element data depending on the known scenario data to form a set of elements, wherein each element is related to at least an actor and the behavior of the actor; computing subsets of elements by combining at least some of the elements of the set in dependence on their corresponding behavior; generating new scenario data related to new scenarios depending on the subsets of elements; and comparing the known scenario data with the new scenario data in order to identify the unknown scenarios. | 11-05-2009 |
20090276383 | RULES GENERATION FOR IT RESOURCE EVENT SITUATION CLASSIFICATION - A computer processing device receives computer readable data to derive computer executable rules for mining and constructing situation categories. The received data is transformed into a predetermined standard format if the received data is not already in the predetermined standard format. The predetermined standard formatted data is parsed, and an outer, iterative loop is performed until at least one predetermined stopping criterion is met. An inner iterative loop is performed within the outer iterative loop until all desired subsets of data are processed. During the inner iterative loop, selected subsets of data are labeled with labels associated with corresponding previously labeled subsets of data. New computer executable rules are generated for mining and constructing situation categories from the labeled subsets of data. Keyword list classifiers are transformed using the stored labeled subsets of data. | 11-05-2009 |
20090281969 | Decision Tree Representation of a Function - An arbitrary function may be represented as an optimized decision tree. The decision tree may be calculated, pruned, and factored to create a highly optimized set of equations, much of which may be represented by simple circuits and little, if any, complex processing. A circuit design system may automate the decision tree generation, optimization, and circuit generation for an arbitrary function. The circuits may be used for processing digital signals, such as soft decoding and other processes, among other uses. | 11-12-2009 |
20090281970 | AUTOMATED TAGGING OF DOCUMENTS - An automated technique for tagging documents includes using a semantic tagger to generate an annotation that associates a standard tag with a first text fragment of the user-defined document, wherein the tagger is trained on a standard document annotated with a standard tag, associating the first user-defined tag with a second text fragment of the user-defined document in response to the second text fragment matching a value associated with the first user-defined tag, and establishing a mapping between the standard tag and the first user-defined tag in response to existence of a requisite correlation between the standard tag and the user-defined tag. The technique may further include selecting from the user-defined document a tagged text fragment that is associated with a second user-defined tag, and providing the tagged text fragment and a standard tag associated by the mapping with the second user-defined tag to the tagger as additional training input. | 11-12-2009 |
20090281971 | SYSTEM AND METHOD FOR CLASSIFYING DATA STREAMS WITH VERY LARGE CARDINALITY - Systems and methods for object classification are provided. An object is identified along with the attributes that describe that object. These attributes are grouped into attribute patterns. Classes to be used in the classification are also identified. For each identified class a sketch table containing a plurality of parallel hash tables is created and trained using known objects with known classifications. For the object to be classified, each attribute pattern is processed using the all of the hash functions for each sketch table. This results in a plurality of values under each sketch table for a single attribute pattern. The lowest value is selected for each sketch table. The distribution of values across all sketch tables is evaluated for each attribute pattern. This produces a discriminatory power for each attribute pattern. Those attribute patterns having a discriminatory power above a given threshold are selected. The selected attribute patterns and associated sketch table values are added. The sketch table with the largest overall sum is identified, and the class associated with that sketch table is assigned to the object to which the attribute patterns belong. | 11-12-2009 |
20090287620 | SYSTEM AND METHOD FOR OBJECT DETECTION AND CLASSIFICATION WITH MULTIPLE THRESHOLD ADAPTIVE BOOSTING - Systems and methods for classifying a object as belonging to an object class or not belonging to an object class using a boosting method with a plurality of thresholds is disclosed. One embodiment is a method of defining a strong classifier, the method comprising receiving a training set of positive and negative samples, receiving a set of features, associating, for each of a first subset of the set of features, a corresponding feature value with each of a first subset of the training set, associating a corresponding weight with each of a second subset of the training set, iteratively i) determining, for each of a second subset of the set of features, a first threshold value at which a first metric is minimized, ii) determining, for each of a third subset of the set of features, a second threshold value at which a second metric is minimized, iii) determining, for each of a forth subset of the set of features, a number of thresholds, iv) determining, for each of a fifth subset of the set of features, an error value based on the determined number of thresholds, v) determining the feature having the lowest associated error value, and vi) updating the weights, defining a strong classifier based on the features having the lowest error value at a plurality of iterations, and classifying a sample as either belonging to an object class or not belonging to an object class based on the strong classifier. | 11-19-2009 |
20090287621 | Forward feature selection for support vector machines - In one embodiment, the present invention includes a method for training a Support Vector Machine (SVM) on a subset of features (d′) of a feature set having (d) features of a plurality of training instances to obtain a weight per instance, approximating a quality for the d features of the feature set using the weight per instance, ranking the d features of the feature set based on the approximated quality, and selecting a subset (q) of the features of the feature set based on the ranked approximated quality. Other embodiments are described and claimed. | 11-19-2009 |
20090287622 | System and Method for Active Learning/Modeling for Field Specific Data Streams - A system and method for determining whether at least one data point is interesting may be provided. The system may include, among other things, a memory for the at least one data point and a query-by-transduction module configured to assign a plurality of labels to the at least one data point, wherein each label among the plurality of labels corresponds to a respective classification for the at least one data point and wherein each label corresponds to a respective confidence metric that indicates a level of confidence that the respectively corresponding label accurately classifies the at least one data point, analyze the plurality of confidence metrics, and determine whether the at least one data point is interesting based on the analysis. | 11-19-2009 |
20090292660 | USING RULE INDUCTION TO IDENTIFY EMERGING TRENDS IN UNSTRUCTURED TEXT STREAMS - A method for identifying emerging concepts in unstructured text streams comprises: selecting a subset V of documents from a set U of documents; generating at least one Boolean combination of terms that partitions the set U into a plurality of categories that represent a generalized, statistically based model of the selected subset V wherein the categories are disjoint inasmuch as each document of U is included in only one category of the partition; and generating a descriptive label for each of the disjoint categories from the Boolean combination of terms for that category. | 11-26-2009 |
20090299924 | INTELLIGENT HUMAN-MACHINE INTERFACE - Embodiments in accordance with the present invention relate to methods and apparatus for an intelligent human-machine interface. By way of example, but not limited thereto, embodiments of methods and apparatus are presented of an intelligent human-machine interface for the operating room, and more particularly, to systems and processes for real-time management and feedback of process control, situational awareness, logistics, communication, and documentation, herein referred to as system. One element of the system, among others, provides a knowledge base that organizes information and rules that enables an accurate, relevant and timely decision support system. The knowledge base is represented in a hierarchical structure of functions and systems. The system serves as platform for the avoidance, detection and timely correction of errors, and as such, acts as a countermeasure to error. | 12-03-2009 |
20090299925 | Automatic Detection of Undesirable Users of an Online Communication Resource Based on Content Analytics - An exemplary processor-implemented method of determining whether a user of an online communication resource is an undesirable user includes the steps of building at least one model based on at least one feature of a feature set using at least one machine learning technique; and classifying the user by comparing at least one feature of the feature set that is associated with the user to the at least one model, a determination as to whether the user is an undesirable user being based at least in part on the classification of the user. | 12-03-2009 |
20090307160 | PARALLEL GENERATION OF A BAYESIAN NETWORK - A method for generating a Bayesian network in a parallel manner is based on an initial model having a plurality of nodes. Each node corresponds to a variable of a data set and has a local distribution associated therewith. The method includes assigning a plurality of subsets of the nodes to a respective plurality of constructors. The plurality of constructors is operated in a parallel manner to identify edges to add between nodes in the initial model. The identified edges are added to the initial model to generate the Bayesian network. The edges indicate dependency between nodes connected by the edges. | 12-10-2009 |
20090307161 | SYSTEM AND METHOD TO LEARN AND DEPLOY AN OPTIMAL USER EXPERIENCE IN AN ONLINE SYSTEM - Methods and systems to learn an optimal user experience. The system receives a request over a network from a user. The request includes context information. The system identifies a response to the request is to be utilized to learn whether a first interface component included in a first plurality of interface components is an optimal choice for a first decision. The response includes an interface. The interface includes the first interface component. The system identifies the response to the request is to be utilized based on the context information. Finally, the system communicates the response over the network to the user. | 12-10-2009 |
20090307162 | METHOD AND APPARATUS FOR AUTOMATED ASSISTANCE WITH TASK MANAGEMENT - The present invention relates to a method and apparatus for assisting with automated task management. In one embodiment, an apparatus for assisting a user in the execution of a task, where the task includes one or more workflows required to accomplish a goal defined by the user, includes a task learner for creating new workflows from user demonstrations, a workflow tracker for identifying and tracking the progress of a current workflow executing on a machine used by the user, a task assistance processor coupled to the workflow tracker, for generating a suggestion based on the progress of the current workflow, and a task executor coupled to the task assistance processor, for manipulating an application on the machine used by the user to carry out the suggestion. | 12-10-2009 |
20090313188 | Computationally Efficient Signal Classifier - Methods provided by this description may include receiving input signals for classification, and deriving specified signal parameters from the input signals. These methods may also compare the specified signal parameter to signal parameters derived from training signals, with the training signals being associated with predefined signal classes. These methods may also classify the input signals based on this comparison of the signal parameters, as derived respectively from the input signals in the training signals. | 12-17-2009 |
20090313189 | METHOD, SYSTEM AND APPARATUS FOR ASSEMBLING AND USING BIOLOGICAL KNOWLEDGE - Disclosed are methods, systems and apparatus for constructing assemblies of biological knowledge constituting a biological knowledge base, and for subsetting and transforming life sciences-related data and information into biological models to facilitate computation and electronic reasoning on biological information. A subset of data is extracted from a global knowledge base or repository to reconstruct a more specialized sub-knowledge base or assembly designed specifically for the purpose at hand. Assemblies generated by the invention permit selection and rational organization of seemingly diverse data into a model of any selected biological system, as defined by any desired biological criteria. These assemblies can be mined easily and can be logically reasoned with great productivity and efficiency. | 12-17-2009 |
20090313190 | COGNITIVE OPERATING SYSTEM - An operating system configured to support cognitive capable environments is described. The system comprises a memory structure and an I/O process configured to update inputs and outputs. The operating system further includes a process to determine changes to symbols within the symbol space due to the stimulus and create STM images of the inputs. Additional processes are configured to pass stimulations between connecting symbols, measure temporal and spatial properties of images, filter the STM images based upon the properties, and propagate stimuli through hereditary structures. Further processes analyze propagated symbol groups of STM images for novel distinctions, create new symbols, connect novel stimuli together for symbol groupings, update existing symbols to include connections to the new symbols, and assign weights to the connections. Additional processes form stimulus-response pairs from received images, provide time-based erosion of connection weights of symbols and adapt learned responses to long term memories. | 12-17-2009 |
20090319448 | METHOD AND SYSTEM FOR POSITIONING - A positioning method includes: first, receiving wireless signals respectively at the positions of a number of training positions so as to extract a number of signal characteristics; next, establishing a positioning database according to the relationship between the training positions and the corresponding positioning module; then, classifying the training positions and the corresponding signal characteristics into a plurality of clusters, wherein when conducting positioning on a positioning node, a characteristic matching is conducted to find out a major cluster most similar to the positioning node; after that, conducting the characteristic matching between the positioning node and the training positions in the major cluster to decide a most-likely position of the positioning node. In addition, the present invention also provides a positioning system using the above-mentioned method. | 12-24-2009 |
20090319449 | PROVIDING CONTEXT FOR WEB ARTICLES - An overwhelming number of articles are available everyday via the internet. Unfortunately, it is impossible to peruse more than a handful, and it is difficult to ascertain an article's social context. The techniques disclosed herein address this problem by harnessing implicit and explicit contextual information from social media. By extracting text surrounding a hyperlink to an article in a post and assessing the article as a function of content surrounding the hyperlink, an article's social context is determined and presented. Additionally, articles that are sufficiently similar in content may be grouped to establish a many-to-one relationship between posts and an article, creating a more accurate assessment. | 12-24-2009 |
20090319450 | PROTEIN SEARCH METHOD AND DEVICE - A protein search method for searching for, as a target protein, a protein having direct or indirect relevance to information based on protein representation profiling data acquired by means of proteome analysis includes: determining, as a target protein, a protein that is relevant to the information based on significance of proteins obtained by using supervised learning from the information and the protein representation in the profiling data; and evaluating the performance of the target protein by means of evaluation data. | 12-24-2009 |
20090319451 | PATTERN CLASSIFICATION METHOD - For assigning a test pattern to a class chosen from a predefined set of classes, the class membership probability for the test pattern is calculated as well as the confidence interval for the class membership probability based upon a number of training patterns in a neighbourhood of the test pattern in the feature space. The number of training patterns in the neighbourhood of the test pattern is obtained from computing a convolution of a density function of the training patterns with a Gaussian smoothing function centred on the test pattern, where the density function of the training patterns is represented as a mixture of Gaussian functions. The convolution of the smoothing function and the mixture of Gaussian functions can be expressed analytically. | 12-24-2009 |
20090319452 | SYSTEM AND METHOD FOR AUTOMATICALLY LEARNING MAILBOX CONFIGURATION CONVENTIONS - A system and method automatically learns mailbox configuration conventions. The validator module determines a valid set of configuration parameters used for accessing an electronic mailbox of a user within a mail domain after receiving configuration information from the user that is limited in the configuration parameters required for accessing the electronic mailbox. A learner module accepts from the validator module a set of configuration parameters determined to be valid and generates configuration conventions for a mail domain. A database store is the generated configuration conventions. The validator and learner modules can be operative as part of a web server. | 12-24-2009 |
20090327170 | Methods of Clustering Gene and Protein Sequences - The invention relates to methods for clustering gene and protein sequences. In particular, it involves generation of networks of sequences where the interconnections are based upon a measure of similarity. The invention also provides methods of optimizing and improving the networks by re-wiring of the network based upon overlap of the nearest neighbors of given pairs of nodes. The invention further provides methods of identifying clusters of sequences within the networks and the optimized networks based upon the topology of the network. The clusters identified represent groups of sequences that are related by function and/or evolution. The invention has particular applicability in annotation of sequences in databases and identification of functional homologs which can be very useful for novel therapeutic and diagnostic targets based upon such targets belonging to a cluster or family that contains a known sequence such as a diagnostic sequence, antigen or other therapeutic target. | 12-31-2009 |
20090327171 | RECOGNIZING GESTURES FROM FOREARM EMG SIGNALS - A machine learning model is trained by instructing a user to perform proscribed gestures, sampling signals from EMG sensors arranged arbitrarily on the user's forearm with respect to locations of muscles in the forearm, extracting feature samples from the sampled signals, labeling the feature samples according to the corresponding gestures instructed to be performed, and training the machine learning model with the labeled feature samples. Subsequently, gestures may be recognized using the trained machine learning model by sampling signals from the EMG sensors, extracting from the signals unlabeled feature samples of a same type as those extracted during the training, passing the unlabeled feature samples to the machine learning model, and outputting from the machine learning model indicia of a gesture classified by the machine learning model. | 12-31-2009 |
20090327172 | ADAPTIVE KNOWLEDGE-BASED REASONING IN AUTONOMIC COMPUTING SYSTEMS - A method, information processing system, and network select machine learning algorithms for managing autonomous operations of network elements. A state ( | 12-31-2009 |
20090327173 | METHOD FOR PREDICTING CYCLE TIME - A method for predicting cycle time comprises the steps of: collecting a plurality of known sets of data; using a clustering method to classify the known sets of data into a plurality of clusters; using a decision tree method to build a classification rule of the clusters; building a prediction model of each cluster; preparing data predicted set of data; using the classification rule to determine that to which clusters the predicted set of data belongs; and using the prediction model of the cluster to estimate the objective cycle time of the predicted set of data. Therefore, engineers can beforehand know the cycle time that one lot of wafers spend in the forward fabrication process, which helps engineers to properly arrange the following fabrication process of the lot of wafer. | 12-31-2009 |
20090327174 | TASK HISTORY USER INTERFACE USING A CLUSTERING ALGORITHM - The aspects of the disclosed embodiments include clustering a set of discrete user interface states into groups; presenting the groups on a display of a device; and enabling selection of any state within a presented group, wherein selection of a state returns the user interface to the selected state. | 12-31-2009 |
20090327175 | PHARMACOKINETIC MODELING OF MYCOPHENOLIC ACID - A method of providing a pharmacokinetic model to provide optimize pharmacokinetic data associated with administering a drug to a patient and a method of optimising pharmacokinetic data associated with administering a drug to a patient, data processing apparatus, recording medium and a pharmacokinetic model are disclosed. | 12-31-2009 |
20090327176 | SYSTEM AND METHOD FOR LEARNING - A method of learning discriminant function for predicting label information by using computer includes: receiving training data including attribute data and label information, to create an initial prediction model based on the attribute data and the label information; calculating, based on the initial prediction model used as a discriminant function, a gradient of a loss function, which is differentiable with respect to the discriminant function and satisfies a monotonous convex function, from the discriminant function and the label information; creating a prediction model from the attribute data and the gradient while assuming that the gradient is label information of each sample of the training data; and updating the discriminant function based on the created prediction model. | 12-31-2009 |
20100005040 | FORECASTING ASSOCIATION RULES ACROSS USER ENGAGEMENT LEVELS - A method of determining one or more association rules includes: specifying site-sequence values for users, wherein each user is identified with one of a plurality of engagement levels, and the site-sequence values indicate a sequence from a first site to a second site for at least one user identified with a corresponding engagement level; determining cumulative site-sequence values from the site-sequence values for combinations of pairs of sites and distinct engagement levels; determining likelihood values from the cumulative site-sequence values, wherein the likelihood values characterize probabilities for sequences between sites at distinct engagement levels; determining one or more association rules for pairs of sites from one or more corresponding likelihood values at one or more engagement levels, wherein each association rule indicates a sequential association between a corresponding pair of sites; determining one or more confidence values for the one or more association rules by calculating one or more variations of the likelihood values across the engagement levels; and saving one or more values for the one or more associations rules (e.g., likelihood values or confidence values). | 01-07-2010 |
20100005041 | MACHINE LEARNING BASED VOLUME DIAGNOSIS OF SEMICONDUCTOR CHIPS - A system and method for integrated circuit diagnosis includes partitioning an integrated circuit design into sub-regions according to a structure of the integrated circuit design. A decision function is generated for a sub-region by training a machine learning tool. A sequence of test patterns is applied to a device under test (DUT) to determine responses. If the DUT fails, all the decision functions are evaluated with the errors produced by the DUT. A sub-region whose decision function yielded a highest value is selected to find a defect sub-region in the DUT. | 01-07-2010 |
20100005042 | Support vector regression for censored data - A method of producing a model for use in predicting time to an event includes obtaining multi-dimensional, non-linear vectors of information indicative of status of multiple test subjects, at least one of the vectors being right-censored, lacking an indication of a time of occurrence of the event with respect to the corresponding test subject, and performing regression using the vectors of information to produce a kernel-based model to provide an output value related to a prediction of time to the event based upon at least some of the information contained in the vectors of information, where for each vector comprising right-censored data, a censored-data penalty function is used to affect the regression, the censored-data penalty function being different than a non-censored-data penalty function used for each vector comprising non-censored data. | 01-07-2010 |
20100005043 | ACTIVE LEARNING SYSTEM, ACTIVE LEARNING METHOD AND PROGRAM FOR ACTIVE LEARNING - In order to carry out a learning in which newly acquired data is taken to be more important than data previously accumulated, a function is provided which sets a weight for learning data based on an acquisition order of the learning data. Furthermore, in order to carry out a learning which reflects data acquired in the last cycle and a result with respect to the data, a function is provided which feeds back a result of a learning in the last cycle to a rule and sets a weight for learning data based on a relation between a label of data and a prediction value. | 01-07-2010 |
20100010940 | Method for probabilistic information fusion to filter multi-lingual, semi-structured and multimedia Electronic Content - The invention belongs to the field of information system technology and more specifically in the area of electronic content management. The invention concerns method producing filtering systems of electronic documents that contain text in different languages, e.g. English, French, etc., as well as multimedia elements, e.g. digital images and/or digital video and/or digital excerpts of audio/speech. These documents can be semi-structured, i.e., they can exhibit structural features that are not to be found in non-digital documents, e.g. hyperlinks, or not. | 01-14-2010 |
20100010941 | Computer method and apparatus for classifying objects - A computer classification method and apparatus employs statistical analysis of known objects in the class of interest. For each known object in the class, a respective vector of q bits is formed. Each bit indicates presence or absence of an activity or physical property in the object. The probability that a bit is equal to 1 in the class is then applied to vector representations of test objects and determines probability of the test object belonging to the class. | 01-14-2010 |
20100010942 | INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING METHOD, AND PROGRAM - An information processing device includes a first learning unit, a first error modeling unit, a first error generation unit, and a first estimation unit. The first learning unit learns a first estimation formula for estimating a first target variable of content on the basis of the feature quantity of the content. The first error modeling unit determines a first model of an error generated in the estimation of the first target variable based on the first estimation formula learned by the first learning unit. The first error generation unit generates, with the use of a random number, an error according to the first model determined by the first error modeling unit. The first estimation unit estimates the first target variable of the content by using the first estimation formula learned by the first learning unit and the random number generated by the first error generation unit. | 01-14-2010 |
20100010943 | LEARNING DEVICE, LEARNING METHOD, AND PROGRAM - A learning device includes: a plurality of learning modules, each of which performs update learning to update a plurality of model parameters of a pattern learning model that learns a pattern using input data; model parameter sharing means for causing two or more learning modules from among the plurality of learning modules to share the model parameters; module creating means for creating a new learning module corresponding to new learning data for learning the pattern when the new learning data are supplied as the input data; similarity evaluation means for evaluating similarities among the learning modules after the update learning is performed over all the learning modules including the new learning module; and module integrating means for determining whether to integrate the learning modules on the basis of the similarities among the learning modules and integrating the learning modules. | 01-14-2010 |
20100010944 | MANAGING PERSONAL DIGITAL ASSETS OVER MULTIPLE DEVICES - In a first embodiment of the present invention, a method for managing digital assets of a user over multiple home network-enabled devices, the method comprising: receiving information, from a plurality of home network-enabled personal devices, regarding digital assets accessed by the personal devices, wherein the plurality of personal devices are owned or operated by the user and the information is automatically gathered by each personal device tracking its own usage; storing the information; and providing, to one of the plurality of personal devices, identifications of digital assets accessed by the personal devices by accessing the stored information. | 01-14-2010 |
20100010945 | METHOD AND SYSTEM FOR WEB RESOURCE LOCATION CLASSIFICATION AND DETECTION - A method and system for identifying locations associated with a web resource is provided. The location system identifies three different types of geographic locations: a provider location, a content location, and a serving location. A provider location identifies the geographic location of the entity that provides the web resource. A content location identifies the geographic location that is the subject of the web resource. A serving location identifies the geographic scope that the web page reaches. An application can select to use the type of location that is of particular interest. | 01-14-2010 |
20100017349 | METHOD AND APPARATUS FOR DERIVING PROBABILISTIC MODELS FROM DETERMINISTIC ONES - A Dynamic Bayesian Network provides models that provides emulation of patient data. | 01-21-2010 |
20100017350 | Method and Apparatus for Automatically Structuring Free Form Heterogeneous Data - Techniques are provided for automatically structuring free form heterogeneous data. In one aspect of the invention, the techniques include obtaining free form heterogeneous data, segmenting the free form heterogeneous data into one or more units, automatically labeling the one or more units based on one or more machine learning techniques, wherein each unit is associated with a label indicating an information type, and structuring the one or more labeled units in a format to facilitate one or more operations that use at least a portion of the labeled units, e.g., information technology (IT) operations. | 01-21-2010 |
20100023464 | Systems and methods for parameter adaptation - A method of parameter adaptation is used to modify the parameters of a model to improve model performance. The model separately estimates the contribution of each model parameter to the prediction error. It achieves this by transforming to the time-scale plane the vectors of output sensitivities with respect to model parameters and then identifying the regions within the time-scale plane at which the dynamic effect of individual model parameters is dominant on the output. The method then attributes the prediction error in these regions to the deviation of a single parameter from its true value as the basis of estimating individual parametric errors. The proposed Signature Isolation Method (PARSIM) then uses these estimates to adapt individual model parameters independently of the others, implementing, in effect, concurrent adaptation of individual parameters by the Newton-Raphson method in the time-scale plane. The Signature Isolation Method has been found to have an adaptation precision comparable to that of the Gauss-Newton method for noise-free cases. However, it surpasses the Gauss-Newton method in precision when the output is contaminated with noise or when the measurements are generated by inadequate excitation. | 01-28-2010 |
20100023465 | ACTIVE LEARNING SYSTEM, METHOD AND PROGRAM - A processing unit ( | 01-28-2010 |
20100023466 | RULE LEARNING METHOD, PROGRAM AND APPARATUS - A rule learning method for making a computer perform rule learning processing in machine learning includes firstly calculating an evaluation value of respective features in a training example by using data and weights of the training examples; selecting a given number of features in descending order of the evaluation values; secondly calculating a confidence value for one of the given number of selected features; updating the weights of training example, by using the data and weights of the training examples, and the confidence value corresponding to the one feature; firstly repeating the updating for the remaining features of the given number of features; and secondly repeating, for a given number of times, the firstly calculating, the selecting, the secondly calculating, the updating, and the firstly repeating. | 01-28-2010 |
20100023467 | RULE LEARNING METHOD, PROGRAM, AND DEVICE - A rule learning method in machine learning includes distributing features to a given number of buckets based on a weight of the features which are correlated with a training example; specifying a feature with a maximum gain value as a rule based on a weight of the training example from each of the buckets; calculating a confidence value of the specified rule based on the weight of the training example; storing the specified rule and the confidence value in a rule data storage unit; updating the weights of the training examples based on the specified rule, the confidence value of the specified rule, data of the training example, and the weight of the training example; and repeating the distributing, the specifying, the calculating, the storing, and the updating, when the rule and the confidence value are to be further generated. | 01-28-2010 |
20100030714 | METHOD AND SYSTEM TO IMPROVE AUTOMATED EMOTIONAL RECOGNITION - An automated emotional recognition system includes an emotional state classifier adapted to receive, during an operative phase, an input information stream with embedded information related to emotional states of a person, and to generate a succession of emotional state indications derived from the input information stream. The emotional recognition system further includes a post-processing function, configured to receive at least two emotional state indications of the succession and, for each of said at least two emotional state indications, determine a corresponding emotional state representation in an emotional state representation system. The post-processing function is further configured to combine the emotional state representations of the at least two emotional state indications to obtain an output emotional state indication. | 02-04-2010 |
20100030715 | Social Network Model for Semantic Processing - A social network model, based on data relevant to a user, is used for semantic processing to enable improved entity recognition among text accessed by the user. An entity extraction module of the server, with reference to a general training corpus, general gazetteers, user-specific gazetteers, and entity models, parses text to identify entities. The entities may be, for example, people, organizations, or locations. A social network module of the server builds the social network model implicit in the data accessed by the user. The social network model includes the relationships between entities and an indication of the strength of each relationship. The social network module is also used to disambiguate names and unify entities based on the social network model. | 02-04-2010 |
20100036780 | MACHINE LEARNING - Computer implemented machine learning methods are described. A co-operative learning method involves a first rule based system and a second rule based system. A rule base is generated from input data and recursion data is used to recursively update the rule base as a result of newly received input data. Rule data defining at least one rule and associated data are sent to the second system which determines whether to update its rule base using the transmitted rule data, and if so the recursion data is used to recursively determine the updated rules for its rule base. A father machine learning method for a rule based system, involves receiving time series data, determining whether the data increases or decreases the spatial density for previously existing rules, and if so then creating a new cluster and associated rule, otherwise a new cluster is not created. | 02-11-2010 |
20100036781 | APPARATUS AND METHOD PROVIDING RETRIEVAL OF ILLEGAL MOTION PICTURE DATA - Provided are an apparatus and method for detecting illegal motion picture data. The apparatus includes a key frame extractor for extracting a plurality of key frames from motion picture data, a characteristic value file generator for detecting characteristic values of the extracted key frames and generating a characteristic value file, and an illegality determiner for measuring degree of similarity between a previously stored learning model file and the characteristic value file and determining whether or not the motion picture data is legal according to the degree of similarity. | 02-11-2010 |
20100042559 | METHOD AND APPARATUS FOR AUTOMATED IDENTIFICATION OF SIGNAL CHARACTERISTICS - A method of assessing a signal to identify particular signal characteristics comprises application of machine learning to multi-dimensional histograms derived from multi-tap sampling of the signal. The signal is sampled from at least two tap points to retrieve a sample set, and the at least two tap points are adapted to retrieve distinct samples from the signal, such as time spaced samples or spectrally distinct samples. Multiple sample sets are retrieved from the signal over time. The at least two dimensional histogram is built from the joint probability distribution of the plurality of sample sets. A machine learning algorithm then processes the multi-dimensional histogram, and is trained to predict a value of at least one characteristic of the signal. | 02-18-2010 |
20100042560 | CONTEXT AWARE SOLUTION ASSEMBLY IN CONTACT CENTER APPLICATIONS - An apparatus and a method is provided for receiving help requests to solve a problem on a computer, generating a core problem description and retrieving at least one of contextual or environmental parameters associated with the computer. The method also includes assembling a formalized problem description. The method further includes obtaining previously stored formalized solution steps associated with the problem from a database and building a customized solution including context aware solution records that are tagged with at least one of contextual or environmental dependencies. The method also includes transmitting the customized solution to the computer for execution and monitoring the execution of the customized solution. | 02-18-2010 |
20100042561 | METHODS AND SYSTEMS FOR COST-SENSITIVE BOOSTING - Multi-class cost-sensitive boosting based on gradient boosting with “p-norm” cost functionals” uses iterative example weighting schemes derived with respect to cost functionals, and a binary classification algorithm. Weighted sampling is iteratively applied from an expanded data set obtained by enhancing each example in the original data set with as many data points as there are possible labels for any single instance, and where each non-optimally labeled example is given the weight equaling a half times the original misclassification cost for the labeled example times the p−1 norm of the average prediction of the current hypotheses. Each optimally labeled example is given the weight equaling the sum of the weights for all the non-optimally labeled examples for the same instance. Component classification algorithm is executed on a modified binary classification problem. A classifier hypothesis is output, which is the average of all the hypotheses output in the respective iterations. | 02-18-2010 |
20100042562 | METHOD OF DOWNLOADING USAGE PARAMETERS INTO AN APPARATUS, AND APPARATUS FOR IMPLEMENTING THE INVENTION - Method of downloading usage parameters into an apparatus, and apparatus for implementing the invention After a first start-up, a first appliance performs a self-learning step for generating usage parameters. These parameters are elaborated on subsequent start-ups. When these parameters are optimized, the first appliance transmits them to another appliance which requests them. This second appliance uses the parameters of the first as optimized parameters. In this way, the second appliance limits the duration of the self-learning step and the use of non-optimal parameters. According to a refinement, the optimal parameters are centralized on a server which transmits them to a plurality of second appliances using a transmission network. | 02-18-2010 |
20100042563 | SYSTEMS AND METHODS OF DISCOVERING MIXTURES OF MODELS WITHIN DATA AND PROBABILISTIC CLASSIFICATION OF DATA ACCORDING TO THE MODEL MIXTURE - Discovering mixtures of models includes: initiating learning algorithms, determining, data sets including a cluster of points in a first region of a domain and a set of points distributed near a first line extending across the domain; inferencing parameters from the cluster and the set of points; creating a description of the cluster of points in the first region of the domain and computing approximations of a first learned mixture model and a second learned mixture model; determining a first and second probability, generating a confidence rating that each point of the cluster of points in the first region of the domain corresponds to the first learned mixture model and generating a confidence rating that each point of the set of points distributed near the first line correspond to the second learned mixture model, thus causing determinations of behavior of a system described by the learned mixture models. | 02-18-2010 |
20100049674 | GENERIC CLASSIFICATION SYSTEM - A classification system including a training device and one or more classification device for classifying one or more vectors other than training vectors. The training device is for selecting which training classification algorithms best classifies a set of training vectors, and for finding a set of values, of parameters of a generic classification algorithm, that enable the generic classification algorithm to substantially emulate the selected training classification algorithm. | 02-25-2010 |
20100049675 | Recovery of 3D Human Pose by Jointly Learning Metrics and Mixtures of Experts - Systems and methods are disclosed for determining human pose by generating an Appearance and Position Context (APC) local descriptor that achieves selectivity and invariance while requiring no background subtraction; jointly learning visual words and pose regressors in a supervised manner; and estimating the human pose. | 02-25-2010 |
20100049676 | Arrangement and Method for Network Management - The present invention relates to an arrangement for network management and adapted to be provided in or associated with a network node to be managed. It comprises, or is in communication with, modeling means adapted to, using substantially non-formal descriptions, model network domain and behavior using formal ontologies comprising inference capabilities by means of an inference engine, thus providing a formal ontology model describing domain and behavior. It also comprises annotating means adapted to add semantic information to the formal domain and behavior ontology model, generating means adapted to, using said formal ontology model and said inference engine, elaborate an algorithm adapted to generate and update a probabilistic causal network graph structure representing the domain and its behavior. | 02-25-2010 |
20100049677 | SEQUENCE LEARNING IN A HIERARCHICAL TEMPORAL MEMORY BASED SYSTEM - A hierarchy of computing modules is configured to learn a cause of input data sensed over space and time, and is further configured to determine a cause of novel sensed input data dependent on the learned cause. At least one of the computing modules has a sequence learner module configured to associate sequences of input data received by the computing module to a set of causes previously learned in the hierarchy. | 02-25-2010 |
20100057647 | ACCOMMODATING LEARNED CLAUSES IN RECONFIGURABLE HARDWARE ACCELERATOR FOR BOOLEAN SATISFIABILITY SOLVER - A hardware accelerator is provided for Boolean constraint propagation (BCP) using field-programmable gate arrays (FPGAs) for use in solving the Boolean satisfiability problem (SAT). An inference engine may perform implications. Learned clauses may be generated during conflict analysis. Operations pertaining to learned clauses may include clause insertion and clause deletion (e.g., by invalidation) from a learned clause inference engine, and “garbage collection” in which unused or invalidated clauses may be removed from an inference engine. | 03-04-2010 |
20100057648 | CREATING FORMS WITH BUSINESS LOGIC - An eForm with integrated business logic is created from an existing eForm using a parser to parse a source eForm to extract attributes of items on the source eForm. An item recognition unit recognizes interactive items in the source eForm according to the attributes of the items extracted by the parser. A business logic recognition unit recognizes business logic integrated into the source eForm according to the attributes of the items. An object eForm generator generates an object eForm containing the recognized interactive items and business logic. | 03-04-2010 |
20100057649 | SYSTEM AND METHOD FOR FAULT PREDICTION IN HOME NETWORK - A system for fault prediction in a home network includes: a context generator for generating context information based on status data collected in real time about components of the home network; a specification interpreter for generating knowledge rules for fault detection by using specifications of the components of the home network; a context analyzer for analyzing if the context information meet the knowledge rules to classify the context information into normal situation contexts and abnormal situation contexts; a context pattern learner for generating new knowledge rules based on the abnormal situation contexts and fault rules corresponding to the abnormal situation contexts; a knowledge rule database for storing and managing the knowledge rules and the new knowledge rules; and a fault predictor for analyzing a correlation between the knowledge rules or the new knowledge rules and the generated context information, thereby predicting faults to be generated. | 03-04-2010 |
20100057650 | CHEMICAL REACTION-TYPE METAHEURISTIC - Subject matter disclosed herein relates to various embodiments of a chemical reaction-type metaheuristic. According to an embodiment, solutions to an objective function can be determined by iteratively searching for a minimum energy state of one or more interactions of molecules in a chemical reaction. The molecules in the chemical reaction can be assigned to represent the possible outcomes of the objective function. In a specific embodiment, the interactions of the molecules can modeled as on-wall ineffective collisions, decompositions, inter-molecular ineffective collisions, and synthesis. The type of interaction can affect where the next molecular structure is searched. | 03-04-2010 |
20100057651 | Knowledge-Based Interpretable Predictive Model for Survival Analysis - Knowledge-based interpretable predictive modeling is provided. Expert knowledge is used to seed training of a model by a machine. The expert knowledge may be incorporated as diagram information, which relates known causal relationships between predictive variables. A predictive model is trained. In one embodiment, the model operates even with a missing value for one or more variables by using the relationship between variables. For application, the model outputs a prediction, such as the likelihood of survival for two years of a lung cancer patient. A graphical representation of the model is also output. The graphical representation shows the variables and relationships between variables used to determine the prediction. The graphical representation is interpretable by a physician or other to assist in understanding. | 03-04-2010 |
20100063947 | System and Method for Dynamically Adaptable Learning Medical Diagnosis System - A system and method for determining a likelihood of a disease presence in a particular patient includes a patient history database containing records. Each record includes a plurality of data fields related to a particular patient. An analyzing network is provided having access to the patient history database and having features based on the plurality of data fields included in the records to analyze the plurality of data fields and determine a likelihood of disease presence based on the plurality of features. A learning network is provided that has access to the analyzing network to review the likelihood of disease presence determined by the analyzing network and the plurality of data fields included in the records and automatically identify, evaluate, and add new features to the analyzing network that improve determinations of a likelihood of the disease. | 03-11-2010 |
20100063948 | MACHINE LEARNING METHODS AND SYSTEMS FOR IDENTIFYING PATTERNS IN DATA - Methods for training machines to categorize data, and/or recognize patterns in data, and machines and systems so trained. More specifically, variations of the invention relates to methods for training machines that include providing one or more training data samples encompassing one or more data classes, identifying patterns in the one or more training data samples, providing one or more data samples representing one or more unknown classes of data, identifying patterns in the one or more of the data samples of unknown class(es), and predicting one or more classes to which the data samples of unknown class(es) belong by comparing patterns identified in said one or more data samples of unknown class with patterns identified in said one or more training data samples. Also provided are tools, systems, and devices, such as support vector machines (SVMs) and other methods and features, software implementing the methods and features, and computers or other processing devices incorporating and/or running the software, where the methods and features, software, and processors utilize specialized methods to analyze data. | 03-11-2010 |
20100070435 | Computationally Efficient Probabilistic Linear Regression - A computationally efficient method of performing probabilistic linear regression is described. In an embodiment, the method involves adding a white noise term to a weighted linear sum of basis functions and then normalizing the combination. This generates a linear model comprising a set of sparse, normalized basis functions and a modulated noise term. When using the linear model to perform linear regression, the modulated noise term increases the variance associated with output values which are distant from any data points. | 03-18-2010 |
20100070436 | METHOD AND APPARATUS FOR RECOMMENDING CONTENT ITEMS - A recommendation apparatus comprises a monitoring processor which monitors the presentation of content items. A sample processor determines preference data for different content items by performing the steps of determining a preference value for a content item presented by the presentation unit in response to a first duration for a first section of the content item being presented relative to a total duration of the content item, and if the first duration is less than the total duration, determining if a second section of the content item not being presented corresponds to at least one of an end section and a begin section of the content item; and if so determining a confidence value for the preference value in response to a second duration of the second section. The preference data is used as training data for determining a user preference model which is then used to generate recommendations. | 03-18-2010 |
20100070437 | Information Management for Information Display Systems - A user-customizable information management solution, providing protection (e.g., privacy and/or security protection) for displayed information that may reduce or prevent exposure of sensitive and/or confidential information. In one aspect, a viewing aperture is controlled by the user and provides a view of a subset of the information displayed, where information not within the aperture is blocked or obscured to eliminate or reduce viewability. Optionally, simultaneous use of more than one viewing aperture may be supported. In another aspect, predefined information management instructions are used for determining how to protect a portion or portions of a document. The instructions may specify particular text and/or graphics categories defined by the user as being sensitive. Portions of the document that contain corresponding text and graphics are located, using a software-based search, and are blocked or obscured according to the predefined instructions. Dynamic tuning may be supported, whereby the user dynamically selects additional text/graphics for protecting. | 03-18-2010 |
20100070438 | METHOD FOR PREDICTING INTERACTION BETWEEN PROTEIN AND CHEMICAL - The present invention has an object to provide a method for configuring a pattern recognizer using versatile, readily available data, comprehensive protein data, and comprehensive chemical data and an object to provide a method for predicting an unknown interaction of a pair by the pattern recognizer-configuring method. In particular, an interaction such as the coupling between a protein and a chemical is used as an index; at least one selected from four parameters that are the position of a peak in mass spectrum data obtained from each chemical, the set of the position and intensity of the peak, the distance between two peaks, and the set of the positions and intensities of the two peaks is vectorized for each of a first pair having a first interaction and a second pair having a second interaction; an amino acid sequence of each protein is vectorized; a vector containing elements of the vector derived from each protein and elements of the vector derived from each chemical paired with the protein is created; and a support vector machine (SVM) is applied to this vector and trained to learn them, whereby the pattern recognizer is configured so as to discriminate between a class to which the first pair belong and a class to which the second pair belong. | 03-18-2010 |
20100070439 | LEARNING SYSTEM AND LEARNING METHOD - A learning system according to the present invention includes an event list database for storing a plurality of event lists, each of the event lists being a set including a series of state-action pairs which reaches a state-action pair immediately before earning a reward, an event list managing section for classifying state-action pairs into the plurality of event lists for storing, and a learning control section for updating expectation of reward of a state-action pair which is an element of each of the event lists. | 03-18-2010 |
20100070440 | SV REDUCTION METHOD FOR MULTI-CLASS SVM - An SV reduction method for multi-class SVMs is provided with which a number of SVs contained in the multi-class SVMs can be reduced without becoming trapped in a local minimum optimization solution and the reduction of the SVs can be performed at high precision and high speed. The method includes a step of selecting, from a plurality of initially present support vectors, support vector pairs z | 03-18-2010 |
20100070441 | Method, apparatus, and program for generating prediction model based on multiple regression analysis - An objective variable prediction model based on multiple regression analysis and having high prediction accuracy is generated by a computer. The method includes the steps of: a) constructing an initial sample set from samples whose measured value of an objective variable is known; b) obtaining a calculated value of the objective variable using multiple regression analysis; c) extracting samples whose difference between the measured and the calculated value is not larger than a first value, and calculating a determination coefficient by applying multiple regression analysis to the extracted samples; d) repeating the step c) by changing the first value until the determination coefficient exceeds a second value; and e) performing two-class classification to classify the sub-sample set obtained at the end of the step d) as a first sub-sample set and remaining samples as a second sub-sample set, and calculating a discriminant function. | 03-18-2010 |
20100076911 | Automated Feature Selection Based on Rankboost for Ranking - A method using a RankBoost-based algorithm to automatically select features for further ranking model training is provided. The method reiteratively applies a set of ranking candidates to a training data set comprising a plurality of ranking objects having a known pairwise ranking order. Each round of iteration applies a weight distribution of ranking object pairs, yields a ranking result by each ranking candidate, identifies a favored ranking candidate for the round based on the ranking results, and updates the weight distribution to be used in next iteration round by increasing weights of ranking object pairs that are poorly ranked by the favored ranking candidate. The method then infers a target feature set from the favored ranking candidates identified in the iterations. | 03-25-2010 |
20100076912 | System and Method for Determining a Characteristic of an Individual - A system and method for determining a characteristic of an individual is provided. The method includes determining at least one nonconscious element of an interaction by the individual and correlating the at least one nonconscious element with at least one identifiable demographic characteristic of the individual. The system includes a computerized medium having a human interface system situated to facilitate interaction with the individual and produce a quantity of data corresponding to the interaction. A programmable device is in communication with the computerized medium and is situated to use at least a portion of the quantity of data corresponding to the interaction with the individual to determine at least one nonconscious element of the interaction with the individual. A correlation system is situated to correlate the at least one nonconscious element with at least one identifiable demographic characteristic and output a quantity of resulting information. | 03-25-2010 |
20100082506 | Active Electronic Medical Record Based Support System Using Learning Machines - A data processing technique is provided. In one embodiment, a computer-implemented method includes receiving image data from an imaging system and organizing the image data into multiple objects of interest. The method may also include identifying source-invariant features of the multiple objects of interest and classifying the multiple objects of interest via a learning algorithm into categories based, at least in part, on the identified source-invariant features. Further, the method may include outputting a report based at least in part on data derived from the classification of one or more of the multiple objects of interest. Additional methods, systems, and devices are also disclosed. | 04-01-2010 |
20100082507 | Predicting Performance Of Executing A Query In Isolation In A Database - One embodiment is a method that generates query vectors from query plans and performance vectors from data collected while executing queries in a database. The method then uses a machine learning technique (MLT) to compute distances between two query vectors and two performance vectors and to predict performance of executing a new single query in isolation in the database. | 04-01-2010 |
20100082508 | Method for tagging of a content and a corresponding system - A method generates tag proposals for tagging of a content, wherein the generating of said tag proposals is performed by combining at least two tag proposing procedures in dependence of a work context of a user. The method can be applied with regard to each area where tagging of contents is desired. By use of the method an effective, computing resource saving, and/or flexible tagging is enabled, by which a sufficient number of tags with high quality can be identified. | 04-01-2010 |
20100088255 | METHOD AND SYSTEM FOR DETERMINING THE ACCURACY OF DNA BASE IDENTIFICATIONS - Embodiments disclosed herein relate to a method and system for determining the accuracy of DNA base identifications, based at least partly on sampling characteristics of subsets within training data sets. | 04-08-2010 |
20100088256 | Method and monitoring system for the rule-based monitoring of a service-oriented architecture - The present invention concerns a method for the rule-based monitoring of a component (C | 04-08-2010 |
20100088257 | Systems and Methods for Generating Predicates and Assertions - Systems and methods for deriving a predicate by constructing a logic formula from information recorded during test execution, optimizing the logic formula and computing the logical implication of the optimized logic formula. Systems and methods for deriving an assertion from a logical implication by substituting each predicate in the logical implication with corresponding design elements from a hardware design description, inserting the design elements into a target template, inserting a context-sensitive input of the target template based on design elements in the hardware design description and creating an instance name for an instantiation of the target template. Systems and methods for generating a set of clauses that are implied by a disjunctive normal formula of a set of cubes. | 04-08-2010 |
20100094782 | Information Processing Apparatus, Information Processing Method, and Program - The present invention relates to an information processing apparatus, an information processing method, and a program capable of quickly and accurately creating an algorithm for extracting features from content data such as song data. A feature extraction algorithm creation apparatus includes: a low-level feature extraction expression list creation section 21 that creates as many as “n” low-level feature extraction expression lists each constituted by “m” low-level feature extraction expressions; a low-level feature computation section 24 that substitutes input data of “j” songs into “n” low-level feature extraction expression lists so as to acquire “n” combinations of “m” low-level features corresponding to each input data item; and a high-level feature extraction expression learning section 25 that estimates high-level feature extraction expressions through learning based on training data corresponding to “n” low-level feature outputs (“k” high-level features corresponding to each of “j” songs) . This invention can be applied to systems for acquiring high-level features of songs and videos. | 04-15-2010 |
20100094783 | Method and System for Classifying Data in System with Limited Memory - Embodiments of the invention describe a method for classifying data in a system with limited memory. The method applies exemplar learning (EL) procedures to a training data set to produce an exemplar data set adapted to the size of the memory. The EL procedure is selected form a group consisting of an entropy based exemplar learning (EBEL) procedure and an advanced broadband enabled learning (ABEL) procedure. The exemplar data set is used to classify acquired by the system data. | 04-15-2010 |
20100094784 | GENERALIZED KERNEL LEARNING IN SUPPORT VECTOR REGRESSION - A generalized kernel learning system and method for learning a wide variety of kernels for use in a support vector regression (SVR) technique. Embodiments of the generalized kernel learning system and method learn nearly any possible kernel, subject to minor constraints. The learned kernel then is used to obtain a desired function, which is a function that closely fits training data and has a desired simplicity. Embodiments of the generalized kernel learning method include inputting the training data, reformulating a and a standard SVM ε-SVR primal formulation for a single kernel as two reformulated primal cost functions for multiple kernels, and then reformulating one of the two reformulated primal cost functions as a reformulated dual cost function. A plurality of different regularizer and kernel combinations is evaluated using the reformulated dual cost function, and it is determined which regularizer and kernel combination yields the desired function. | 04-15-2010 |
20100094785 | SURVIVAL ANALYSIS SYSTEM, SURVIVAL ANALYSIS METHOD, AND SURVIVAL ANALYSIS PROGRAM - Disclosed is a survival analysis system for determining an estimated time until an event occurs on the basis of a group of cases each including at least one attribute value indicating a feature value of a case and information on the measured actual time until an event occurs. The survival analysis system includes: an estimator creating section for creating an estimator for estimating whether or not an event occurs according to the attributes of the group of cases for each actual time; an estimator selecting section for judging whether or not the estimator meets a predetermined selection condition and to selecting an estimator used for calculating the estimated time; and a time calculating section for calculating the estimated time by using the estimator selected by the estimator selecting section. | 04-15-2010 |
20100094786 | Smoothed Sarsa: Reinforcement Learning for Robot Delivery Tasks - The present invention provides a method for learning a policy used by a computing system to perform a task, such delivery of one or more objects by the computing system. During a first time interval, the computing system determines a first state, a first action and a first reward value. As the computing system determines different states, actions and reward values during subsequent time intervals, a state description identifying the current sate, the current action, the current reward and a predicted action is stored. Responsive to a variance of a stored state description falling below a threshold value, the stored state description is used to modify one or more weights in the policy associated with the first state. | 04-15-2010 |
20100100510 | DYNAMIC DISCRETE DECISION SIMULATION SYSTEM - A system that enables dynamic discrete decision simulation is provided. Simulation has many advantages in modeling complex systems to facilitate decision making. The innovation discloses a system that integrates an agent-based discrete event simulator, a geographic information system, a rule base, and interactive databases in addition to interfaces and other supporting components. The modules can seamlessly communicate with each other by exchanging a progression of data, and by making a series of deductive decisions through embedded algorithms. The integrated system can be applied to disaster management planning and training. | 04-22-2010 |
20100100511 | CHANGE-POINT DETECTING METHOD AND APPARATUS - To detect a statistical change-point that appears in time-series data with a high accuracy. A first model learning section 102 learns the occurrence probability distribution of time-series data 111 as a first statistical model (for example, a latent Markov model) defined by a finite number of variables including a latent variable. In the subsequent processing, the degree of a temporal change in the probability distribution is computed for each of the probability distribution of the entire first statistical model, its partial probability distribution (the probability distribution of the latent variable and conditional probability distribution contingent on the value of the latent variable), and the probability distribution in which the above plural probability distributions are linearly-combined with a weight. The change-point of the time-series data 111 is detected on the basis of the computed degree of the change. | 04-22-2010 |
20100100512 | METHOD AND ARRANGEMENT FOR RANKING OF LIVE WEB APPLICATIONS - A method of ranking a plurality of live web applications of a communication device is disclosed. The method comprises receiving at least one data stream, each having a content and associated with a corresponding one of the plurality of live web applications, and evaluating the content of the at least one data stream using machine-learning algorithms. The method further comprises updating each of the corresponding live web applications based on the at least one data stream and determining for each of the corresponding live web applications whether any user reaction occurs with the corresponding live web application in association with the updating of the corresponding live web application. The method comprises ranking the plurality of live web applications relative to each other based at least on the evaluation of the content of the at least one data stream and the determinations of whether any user reaction occurred. Corresponding computer program product, arrangement and communication device are also disclosed. | 04-22-2010 |
20100100513 | METHOD AND APPARATUS FOR PROVIDING FAST KERNEL LEARNING ON SPARSE DATA - A method and apparatus based on transposition to speed up learning computations on sparse data are disclosed. For example, the method receives an support vector comprising at least one feature represented by one non-zero entry. The method then identifies at least one column within a matrix with non-zero entries, wherein the at least one column is identified in accordance with the at least one feature of the support vector. The method then performs kernel computations using successive list merging on the at least one identified column of the matrix and the support vector to derive a result vector, wherein the result vector is used in a data learning function. | 04-22-2010 |
20100114802 | SYSTEM AND METHOD FOR AUTOMATICALLY DISTINGUISHING BETWEEN CUSTOMERS AND IN-STORE EMPLOYEES - An approach that automatically distinguishes between in-store customers and in-store employees is provided. In one embodiment, there is a learning tool configured to construct a model for an in-store employee; and a classifying tool, further comprising matching tool configured to: match attributes between a particular person and the constructed models for an in-store employee, the classifying tool configured to: classify persons into categories of employees and customers based on amount of matching attributes between a particular person and the model for an in-store employee. | 05-06-2010 |
20100114803 | APPARATUS AND METHOD FOR MODELING USER'S SERVICE USE PATTERN - Provided are an apparatus and method for learning and modeling a user's service use pattern. The method includes: collecting information about a service selected by the user and situation information of the user when selecting the service; learning the user's service use pattern based on the collected information; and updating a learning value of a corresponding context-service pair in a user model, which is comprised of context-service pairs, based on the learning result, wherein the situation information of the user includes one or more contexts. | 05-06-2010 |
20100114804 | REPRESENTATIVE HUMAN MODEL GENERATION METHOD - A representative human model generation method is provided. The method includes i) setting a design target population, ii) setting a target accommodating percentage of the design target population, iii) converting anthropometric sizes of the design target population to normalized squared distances, iv) setting a boundary region for a target accommodation percentage of the design target population using normalized squared distances, and v) forming a minimum number of clusters satisfying the target accommodation percentage by performing cluster analysis for anthropometric cases contained in the boundary region among the design target population. | 05-06-2010 |
20100121790 | METHOD, APPARATUS AND COMPUTER PROGRAM PRODUCT FOR CATEGORIZING WEB CONTENT - An apparatus for providing web content categorization may include a processor configured to receive an indication of a web page to be evaluated, evaluate the web page based on characteristics of the web page in relation to previously categorized web pages assigned to respective ones of a structured group of categories, and assign the web page to at least one of the categories based on the evaluation. A corresponding method and computer program product are also provided. | 05-13-2010 |
20100121791 | SYSTEM, METHOD AND PROGRAM FOR PHARMACOKINETIC PARAMETER PREDICTION OF PEPTIDE SEQUENCE BY MATHEMATICAL MODEL - The present invention relates to the system, method and program for the pharmacokinetic parameter prediction of peptide sequence by the mathematical model. | 05-13-2010 |
20100121792 | Directed Graph Embedding - Directed graph embedding is described. In one implementation, a system explores the link structure of a directed graph and embeds the vertices of the directed graph into a vector space while preserving affinities that are present among vertices of the directed graph. Such an embedded vector space facilitates general data analysis of the information in the directed graph. Optimal embedding can be achieved by measuring local affinities among vertices via transition probabilities between the vertices, based on a stationary distribution of Markov random walks through the directed graph. For classifying linked web pages represented by a directed graph, the system can train a support vector machine (SVM) classifier, which can operate in a user-selectable number of dimensions. | 05-13-2010 |
20100121793 | PATTERN GENERATION METHOD, PATTERN GENERATION APPARATUS, AND PROGRAM - Disclosed is an apparatus that generates automatically a characteristic pattern in time series data by clustering a plurality of time series subsequences generated from the time series data. The apparatus includes a time series subsequence generation unit that generates a plurality of time series subsequences from the time series data, a phase alignment unit that aligns a phase of the generated time series subsequence, a clustering unit that performs clustering of a plurality of the time series subsequences, each having a phase aligned, a storage apparatus that stores the pattern obtained by the clustering, and an output apparatus that outputs the stored pattern. | 05-13-2010 |
20100125539 | Hybrid audio-visual categorization system and method - Meta-data (tags) for an audiovisual file can be generated by prompting a user to input certain tags (meta-data) descriptive of the audiovisual file, to serve as an initial estimate of the tags, and then revising the initial estimate (notably to expand it and/or render it more precise) based on the assumption that the relationships which hold between the different tags for a set of manually-tagged training examples will also hold for the tags of the input file now being tagged. | 05-20-2010 |
20100125540 | System And Method For Providing Robust Topic Identification In Social Indexes - A computer-implemented method for providing robust topic identification in social indexes is described. Electronically-stored articles and one or more indexes are maintained. Each index includes topics that each relate to one or more of the articles. A random sampling and a selective sampling of the articles are both selected. For each topic, characteristic words included in the articles in each of the random sampling and the selective sampling are identified. Frequencies of occurrence of the characteristic words in each of the random sampling and the selective sampling are determined. A ratio of the frequencies of occurrence for the characteristic words included in the random sampling and the selective sampling is identified. Finally, for each topic, a coarse-grained topic model is built, which includes the characteristic words included in the articles relating to the topic and scores assigned to those characteristic words. | 05-20-2010 |
20100131436 | Soliciting data indicating at least one subjective user state in response to acquisition of data indicating at least one objective occurrence - A computationally implemented method includes, but is not limited to: acquiring objective occurrence data including data indicating occurrence of at least one objective occurrence; soliciting, in response to the acquisition of the objective occurrence data, subjective user state data including data indicating occurrence of at least one subjective user state associated with a user; acquiring the subjective user state data and correlating the subjective user state data with the objective occurrence data. In addition to the foregoing, other method aspects are described in the claims, drawings, and text forming a part of the present disclosure. | 05-27-2010 |
20100131437 | Correlating data indicating subjective user states associated with multipleusers with data indicating objective occurrences - A computationally implemented method includes, but is not limited to acquiring subjective user state data including data indicating incidence of at least a first subjective user state associated with a first user and data indicating incidence of at least a second subjective user state associated with a second user; acquiring objective occurrence data including data indicating incidence of at least a first objective occurrence and data indicating incidence of at least a second objective occurrence; and correlating the subjective user state data with the objective occurrence data. In addition to the foregoing, other method aspects are described in the claims, drawings, and text forming a part of the present disclosure. | 05-27-2010 |
20100131438 | Medical Ontologies for Computer Assisted Clinical Decision Support - Medical ontology information is used for mining and/or probabilistic modeling. A domain knowledge base may be automatically or semi-automatically created by a processor from a medical ontology. The domain knowledge base, such as a list of disease associated terms, is used to mine for corresponding information from a medical record. The relationship of different terms with respect to a disease may be used to train a probabilistic model. Probabilities of a disease or chance of indicating the disease are determined based on the terms from a medical ontology. This probabilistic reasoning is learned with a machine from ontology information and a training data set. | 05-27-2010 |
20100138365 | PORTABLE WIRELESS ENABLED DIGITAL MEDIA FRAME - A wireless enabled digital media frame that can communicate over a wireless wide area network (WWAN) is provided. The wireless enabled digital media frame comprises an internal or external WWAN modem (e.g. GPRS/EDGE/UMTS/HSPA/LTE) such that the one or more media is transferred to the media frame using a wireless connection (e.g. 2G/3G/3.5G/4G). The wireless enabled digital media frame displays the received media files on a display screen. Further, broadcast alerts received over the WWAN and/or calculated current signal strength is also displayed on the display screen. Furthermore, setting information is received by the WWAN modem and accordingly applied to modify or update media frame functions. | 06-03-2010 |
20100138366 | SYSTEM AND METHOD FOR INFORMATION PROCESSING AND MOTOR CONTROL - The present invention relates to a system and method for information process and motor control using artificially constructed apparatus. More specially, the present invention provides a system and method that can process nature language and other informational input including visual, audio and other sensory inputs and respond intelligently. | 06-03-2010 |
20100138367 | SYSTEM, METHOD, AND PROGRAM FOR GENERATING NON-DETERMINISTIC FINITE AUTOMATON NOT INCLUDING e-TRANSITION - An initial setting unit receives from an input device a syntax tree generated from a regular expression, and initializes an NFA and an NFA converting section that applies five conversion patterns to each node of the syntax tree to directly convert the node into an NFA not including ε-transition. When the conversion is finished, the NFA converting section outputs the NFA generated to an output device. | 06-03-2010 |
20100138368 | METHODS AND SYSTEMS FOR SELF-IMPROVING REASONING TOOLS - Implementations that integrate data-driven modeling and knowledge into self-improving reasoning systems and processes are described. For example, an implementation of a method may include determining at least one recommended action using a reasoning component having a data-driven modeling portion and a knowledge-based portion. Such determining includes integrating one or more determination aspects determined by the data-driven modeling portion, and one or more additional determination aspects determined by the knowledge-based portion. | 06-03-2010 |
20100138369 | LEARNING APPARATUS, LEARNING METHOD, INFORMATION MODIFICATION APPARATUS, INFORMATION MODIFICATION METHOD, AND PROGRAM - A content modification unit modifies an input image in accordance with a user operation, and generates modification information necessary for outputting a resulting output image. A modification information recording unit accumulates a plurality of pieces of modification information corresponding to the number of times an operation is performed by a user. A learning unit uses the plurality of pieces of modification information accumulated in the modification information recording unit as student data, and performs learning using teacher data acquired by a teacher data acquisition unit to calculate a prediction coefficient representing the feature of the user operation, and stores the prediction coefficient in a user algorithm recording unit. The present invention can be applied to, for example, an image processing apparatus. | 06-03-2010 |
20100138370 | METHOD AND APPARATUS FOR MACHINE-LEARNING BASED PROFILING - A method and system for profiling a user based upon a user's previous on-line actions is provided. The profile provides a characterization of the user's preferences based upon a received user event. The user event identifying event identification information and a user identifier. A look-up in a cached web map is performed to retrieve classification information associated with the event identification information. A user profile is retrieved or created for the user identifier. Profile update information is generated based upon the retrieved classification information for the user event, to identify how the user is to be updated based upon the retrieved classification information and defined profiling rules. The user profile is updated and stored for access by an external advertising server. The classification information provides a text-score record comprising a text string and a score defined in relation to a lexical ontology comprising a hierarchy of categories. | 06-03-2010 |
20100138371 | INFORMATION PROCESSING APPARATUS AND UPDATE INFORMATION OBTAINMENT METHOD - An information processing apparatus includes an obtainment section that obtains, via a network, information updated at a distribution origin on the network; and a determination section that determines an obtainment rule relating to a timing of the obtainment by the obtainment section of update information for the distribution origin, wherein the obtainment section obtains update information based on a predetermined learning rule for a first distribution origin for which obtainment rule has not been determined by the determination section, the determination section determines an obtainment rule for the first distribution origin based on a result of the obtainment by the obtainment section of update information from the distribution origin based on the learning rule, and the obtainment section, in response to the determination of the obtainment rule by the determination section, obtains update information from the first distribution origin based on the obtainment rule. | 06-03-2010 |
20100145891 | GENERATING EXTENDED DATA FROM A PATTERN-RECOGNITION MODEL FOR A COMPUTER SYSTEM - Some embodiments of the present invention provide a system that generates extended data for a pattern-recognition model used in electronic prognostication for a computer system. During operation the system determines, for each sensor in a set of sensors, a regression coefficient between training data from the sensor and training data from each of the other sensors in the set of sensors. Next, for each sensor in the set of sensors, the system stretches the training data from each of the other sensors by a predetermined amount, and generates extended data for the sensor based on the stretched training data for each of the other sensors and the regression coefficients between training data from the sensor and training data from each of the other sensors. | 06-10-2010 |
20100145892 | SEARCH DEVICE AND ASSOCIATED METHODS - A search device and associated methods use music emotions to browse, organize and retrieve music collections. The search device comprises a processor and an interface. The processor uses machine learning techniques to determine music emotion according to music features and organizes music by emotions for browsing and retrieving music collections. The interface connects to the processor and allows a person to retrieve desired music from the processor. Methods associated with the search device comprise a processor initialization method, a method of loading new music into the search device and several methods of retrieving desired music from the search device. | 06-10-2010 |
20100145893 | GENOMIC CLASSIFICATION OF NON-SMALL CELL LUNG CARCINOMA BASED ON PATTERNS OF GENE COPY NUMBER ALTERATIONS - The invention is directed to methods and kits that allow for classification of non-small cell lung carcinoma tumors and cell lines according to genomic profiles, and methods of diagnosing, predicting clinical outcomes, and stratifying patient populations for clinical testing and treatment using the same. | 06-10-2010 |
20100145894 | GENOMIC CLASSIFICATION OF COLORECTAL CANCER BASED ON PATTERNS OF GENE COPY NUMBER ALTERATIONS - The invention is directed to methods and kits that allow for classification of colorectal cancer cells according to genomic profiles, and methods of diagnosing, predicting clinical outcomes, and stratifying patient populations for clinical testing and treatment using the same. | 06-10-2010 |
20100145895 | COMPONENT RELIABILITY BUDGETING SYSTEM - A system may include acquisition of a supply voltage information representing past supply voltages supplied to an electrical component, acquisition of a temperature information representing past temperatures of the electrical component, and control of a performance characteristic of the electrical component based on the supply voltage information and the temperature information. Some embodiments may further include determination of a reliability margin based on the supply voltage information, the temperature information, and on a reliability specification of the electrical component, and change of the performance characteristic based on the reliability margin. | 06-10-2010 |
20100145896 | COMPOUND PROPERTY PREDICTION APPARATUS, PROPERTY PREDICTION METHOD, AND PROGRAM FOR IMPLEMENTING THE METHOD - A compound property prediction apparatus includes a training sample library ( | 06-10-2010 |
20100153314 | SYSTEMS AND METHODS FOR COLLABORATIVE FILTERING USING COLLABORATIVE INDUCTIVE TRANSFER - Systems and methods are disclosed that are configured to access a database that includes a list of members of a first group, a list of members of a second group, and ratings for at least some of the members of the second group. The ratings are attributed to the members of the first group. A machine learning training set is built for a particular member of the first group. The training set includes class labels corresponding to the particular member's ratings for the members of the second group, and features that include supplied and predicted ratings from at least a subset of processed members of the first group. A predictor for the particular member of the first group is trained based on the machine learning training set. The predictor corresponding to the particular member is used to generate predicted ratings for one or more members of the second group the particular member has not rated. | 06-17-2010 |
20100153315 | BOOSTING ALGORITHM FOR RANKING MODEL ADAPTATION - Model adaptation may be performed to take a general model trained with a set of training data (possibly large), and adapt the model using a set of domain-specific training data (possibly small). The parameters, structure, or configuration of a model trained in one domain (called the background domain) may be adapted to a different domain (called the adaptation domain), for which there may be a limited amount of training data. The adaption may be performed using the Boosting Algorithm to select an optimal basis function that optimizes a measure of error of the model as it is being iteratively refined, i.e., adapted. | 06-17-2010 |
20100153316 | SYSTEMS AND METHODS FOR RULE-BASED ANOMALY DETECTION ON IP NETWORK FLOW - A system to detect anomalies in internet protocol (IP) flows uses a set of machine-learning (ML) rules that can be applied in real time at the IP flow level. A communication network has a large number of routers that can be equipped with flow monitoring capability. A flow collector collects flow data from the routers throughout the communication network and provides them to a flow classifier. At the same time, a limited number of locations in the network monitor data packets and generate alerts based on packet data properties. The packet alerts and the flow data are provided to a machine learning system that detects correlations between the packet-based alerts and the flow data to thereby generate a series of flow-level alerts. These rules are provided to the flow time classifier. Over time, the new packet alerts and flow data are used to provide updated rules generated by the machine learning system. | 06-17-2010 |
20100153317 | Intelligent robot and control method thereof - Disclosed herein are a robot with a judgment system to enable implementation of multi-dimensional recognitions, thoughts and actions, and a control method thereof. The judgment system includes a dialog system and a task-planning system. The dialog system includes a dialog manager to manage the progress of a dialog of the intelligent robot with a user. The task-planning system includes a leader agent, an action agent and an interaction agent and serve to control a goal, plan and action of a task to be performed by the intelligent robot based on the dialog. The judgment system assists separation of concerns and consequently, enhances convenience of development. The judgment system of the robot contains a mechanism that considers a great number of cases, such as a task priority, immediate user input, inherent robot task, etc., enabling implementation of multi-dimensional recognitions, thoughts and actions. | 06-17-2010 |
20100153318 | Methods and systems for automatically summarizing semantic properties from documents with freeform textual annotations - Some embodiments are directed to identifying semantic properties of documents using free-text annotations associated with the documents. Semantic properties of documents may be identified by using a model that is trained on a corpus of training documents where one or more of the training documents may include free-text annotations. In some embodiments, the model may identify semantic topics expressed only in free-text annotations or only in the body of a document. The model may applied to identify semantic topics associated with a work document or to summarize the semantic topics present in a plurality of work documents. | 06-17-2010 |
20100153319 | METHODS, APPARATUS AND ARTICLES OF MANUFACTURE TO CHARACTERIZE APPLICATIONS - Example methods, apparatus and articles of manufacture to characterize applications are disclosed. A disclosed example method includes collecting resource utilization trace data from the two or more applications simultaneously running on one or more computational devices, determining an intrinsic dimensionality of the collected trace data, the intrinsic dimensionality representing a number of predominate features that substantially characterize the trace data, and characterizing each application's workload based on the determined intrinsic dimensionality. | 06-17-2010 |
20100153320 | METHOD AND ARRANGEMENT FOR SIM ALGORITHM AUTOMATIC CHARSET DETECTION - The invention relates, in an embodiment, to a computer-implemented method for handling a target document, the target document having been transmitted electronically and involving an encoding scheme. The method includes training, using a plurality of text document samples, to obtain a set of machine learning models. Training includes using SIM (Similarity Algorithm) to generate the set of machine learning models from feature vectors obtained from the plurality of text document samples. The method also includes applying the set of machine learning models against a set of target document feature vectors converted from the target document to detect the encoding scheme. The method including decoding the target document to obtain decoded content of the document based on at least the first encoding scheme. | 06-17-2010 |
20100161523 | GENERATION AND USE OF SPECIFIC PROBABILITY TABLES FOR ARITHMETIC CODING IN DATA COMPRESSION SYSTEMS - In one embodiment, when executing data compression or decompression for a data set, a particular compression category of the data set is determined, and a corresponding probability table specific to the particular compression category of the data set is accessed. Then, one of either arithmetic coding (e.g., an encoder device) or decoding (e.g., a decoder device) may be performed on the data set based on the specific probability table. Specifically, in one or more other embodiments, techniques may statistically generate probability tables specific to particular compression categories. | 06-24-2010 |
20100161524 | METHOD AND SYSTEM FOR IDENTIFYING GRAPHICAL MODEL SEMANTICS - A system and method for identifying graphical model semantics, one aspect, receive a graphical diagram, associate each of a plurality of elements with one or more predetermined meta-types, identify a plurality of types in the graphical diagram, and determine a category for each of elements in said graphical diagram. Containment identification rules identify one or more containment relationships in the graphical diagram. Multiplicity identification rules identify multiplicity relationships in the graphical diagram. Advanced semantic rules identify visual elements that represent attributes and refine relationships to identify unique behavior. | 06-24-2010 |
20100161525 | ANALYZING A TARGET ELECTROMAGNETIC SIGNAL RADIATING FROM A COMPUTER SYSTEM - One embodiment of the present invention provides a system that characterizes a computer system parameter by analyzing a target electromagnetic signal radiating from the computer system. First, the system monitors the target electromagnetic signal using a first directional antenna located outside of the computer system, wherein the first directional antenna is directed at a location inside the computer system. The system also monitors the target electromagnetic signal using a second directional antenna located outside of the computer system, wherein a receiving axis of the second antenna is oriented non-parallel to a receiving axis of the first antenna, and wherein the second directional antenna is directed at the location inside the computer system. Next, the system characterizes the computer system parameter based on the target electromagnetic signal received from the first antenna and the target electromagnetic signal received from the second antenna. Then, the system generates a request for an action based on the characterization of the computer system. | 06-24-2010 |
20100161526 | Ranking With Learned Rules - Systems, methods and computer program products for the ranking of a target data set based on learned rules are disclosed. One embodiment is a method that includes generating a learned rule set from a training data record set, creating at least one prototype for each rule in the learned rule set to generate a prototype set, and ranking the target data record set using learned rule set and the prototype set. The generating of a learned rule set includes dividing the training data record set to a positive class and a negative class, and deriving the learned rule set for the positive class. Learning of rules includes deriving the most general projected rules with respect to remaining training data and then refining those rules, eventually selecting the best rules using an F-measure. | 06-24-2010 |
20100161527 | EFFICIENTLY BUILDING COMPACT MODELS FOR LARGE TAXONOMY TEXT CLASSIFICATION - A taxonomy model is determined with a reduced number of weights. For example, the taxonomy model is a tangible representation of a hierarchy of nodes that represents a hierarchy of classes that, when labeled with a representation of a combination of weights, is usable to classify documents having known features but unknown class. For each node of the taxonomy, the training example documents are processed to determine the features for which there are a sufficient number of training example documents having a class label corresponding to at least one of the leaf nodes of a subtree having that node as a root node. For each node of the taxonomy, a sparse weight vector is determined for that node, including setting zero weights, for that node, those features determined to not appear at least a minimum number of times in a given set of leaf nodes in the sub-tree with that node as a root node. The sparse weight vectors can be learned by solving an optimization problem using a maximum entropy classifier, or a large margin classifier with a sequential dual method (SDM) with margin or slack resealing. The determined sparse weight vectors are tangibly embodied in a computer-readable medium in association with the tangible representation of the nodes of the taxonomy. | 06-24-2010 |
20100161528 | Method Of and Apparatus For Automated Behavior Prediction - A computer-implemented method of behavior prediction includes selecting behavior examples having corresponding antecedent candidates, identifying source text descriptions describing the behavior examples, automatically extracting predictors as common themes across all statements and all behavior examples with a language-independent theme extraction process, flagging each behavior example to indicate a presence or absence of the corresponding extracted antecedents in each of the source text descriptions and creating a data array consisting of antecedent columns and behavior example rows, submitting the data array to a pattern classifier to extract patterns among the antecedent candidates and outcomes by training and validating the pattern classifier and predicting a new occurrence of a target behavior by entering a current state of the antecedents to the trained pattern classifier. | 06-24-2010 |
20100161529 | Self-Calibration - Mitigation of processing artefacts caused by surfaces with high contrast printing or colouring transitions within a system to compare signatures derived from inherent physical surface properties of different articles to authenticate or validate articles and within a system to generate signatures from inherent physical surface properties of different articles. | 06-24-2010 |
20100169243 | METHOD AND SYSTEM FOR HYBRID TEXT CLASSIFICATION - A computer-implemented system and method for text classification is provided that applies a hybrid approach for text classification. The system and method includes a text pre-processor which prepares unclassified articles in a format which can be read by a two-stage classifier. The classifier employs a hybrid approach. A keyword-based model achieves machine-labelling of the articles. The machine-labelled articles are used to train a machine learning model. New articles can be applied against the trained model, and classified. | 07-01-2010 |
20100169244 | METHOD AND APPARATUS FOR USING A DISCRIMINATIVE CLASSIFIER FOR PROCESSING A QUERY - A method and apparatus for using a classifier for processing a query are disclosed. For example, the method receives a query from a user, and processes the query to locate one or more documents in accordance with a search engine having a discriminative classifier, wherein the discriminative classifier is trained with a plurality of artificial query examples. The method then presents a result of the processing to the user. | 07-01-2010 |
20100169245 | Statistical Machine Learning - Statistical machine learning, in which an input module receives user input that defines a hypothesis associated with a particular Output. The hypothesis defines one or more starting criteria that are proposed as being correlated with the particular output, and a recommendation engine initially provides recommendations that include the particular output based on the one or more starting criteria defined by the hypothesis. An experience analytics system receives feedback data related to whether the recommendations provided based on the one or more starting criteria defined by the hypothesis were successful and modifies the hypothesis based on the feedback data. Subsequent to the experience analytics system modifying the hypothesis, the recommendation engine provides recommendations that include the particular output based on the modified hypothesis. | 07-01-2010 |
20100169246 | Multimodal system and input process method thereof - A multimodal system and an input processing method thereof are disclosed. The multimodal system includes a pre-constructed input combination constructing unit and an input combination selection unit for selecting an input combination corresponding to an input signal from a user or a sensor. The system performs learning for selecting an input combination from the pre-constructed input combinations. The system provides available input combinations due to this learning, resulting in high satisfaction with the processed result. | 07-01-2010 |
20100169247 | System and method for statistical measurment validation - An apparatus and method are disclosed for a measurement system that reports as a measurement result a confidence interval associated with a histogram bin into which a measurement value falls. The confidence interval is calculated from a subset of training values that also fall within the histogram bin. A training process may be performed in which a plurality of training values is obtained and a mean and standard deviation of the values determined. A plurality of histogram bins are defined from the mean and standard deviation and, for the subsets of training values that fall into each bin, confidence intervals calculated. A need to perform the training process may be determined from a plurality of measured values. | 07-01-2010 |
20100169248 | CONTENT DIVISION POSITION DETERMINATION DEVICE, CONTENT VIEWING CONTROL DEVICE, AND PROGRAM - A content division position determination device | 07-01-2010 |
20100169249 | System and Method for Determining Semantically Related Terms Using an Active Learning Framework - Systems and methods for determining semantically related terms using an active learning framework such as Transductive Experimental Design are disclosed. Generally, to enhance a keyword suggestion tool, an active learning module trains a model to predict whether a term is relevant to a user. The model is then used to present the user with terms that have been determined to be relevant based on the model so that an online advertisement service provider may more efficiently provide a user with terms that are semantically related to a seed set. | 07-01-2010 |
20100169250 | METHODS AND SYSTEMS FOR TRANSDUCTIVE DATA CLASSIFICATION - A system, method, data processing apparatus, and article of manufacture are provided for classifying data. Labeled data points are received, each of the labeled data points having at least one label indicating whether the data point is a training example for data points for being included in a designated category or a training example for data points being excluded from a designated category; receiving unlabeled data points; receiving at least one predetermined cost factor of the labeled data points and unlabeled data points; training a transductive classifier using MED through iterative calculation using the at least one cost factor and the labeled data points and the unlabeled data points as training examples; applying the trained classifier to classify at least one of the unlabeled data points, the labeled data points, and input data points; and outputting a classification of the classified data points, or derivative thereof. | 07-01-2010 |
20100174670 | DATA CLASSIFICATION AND HIERARCHICAL CLUSTERING - Apparatus, systems, and methods can operate to provide efficient data clustering, data classification, and data compression. A method comprises training set of training instances can be processed to select a subset of size-1 patterns, initialize a weight of each size-1 pattern, include the size-1 patterns in classes in a model associated with the training set, and then include a set of top-k size-2 patterns in a way that provides an effective balance between local, class, and global significance patterns. A method comprises processing a dataset to compute an overall significance value of each size-2 pattern in each instance in the dataset, sort the size-2 patterns, and select the top-k size-2 patterns to be represented in clusters, which can be refined into a clustered hierarchy. A method comprises creating an uncompressed bitmap, reordering the bitmap, and compressing the bitmap. Additional apparatus, systems, and methods are disclosed. | 07-08-2010 |
20100174671 | SYSTEM AND METHOD FOR CONCURRENTLY CONDUCTING CAUSE-AND-EFFECT EXPERIMENTS ON CONTENT EFFECTIVENESS AND ADJUSTING CONTENT DISTRIBUTION TO OPTIMIZE BUSINESS OBJECTIVES - The present invention is directed to systems, articles, and computer-implemented methods for assessing effectiveness of communication content and optimizing content distribution to enhance business objectives. Embodiments of the present invention are directed to computer-implemented methods for a computer-implemented method, comprising conducting an experiment using experimental content to determine effectiveness of communication content and executing, while conducting the experiment, a machine learning routine (MLR) using MLR content to enhance an effectiveness metric. | 07-08-2010 |
20100179929 | SYSTEM FOR FINDING QUERIES AIMING AT TAIL URLs - Systems and methodologies for improved query classification and processing are provided herein. As described herein, a query prediction model can be constructed from a set of training data (e.g., diagnostic data obtained from an automatic diagnostic system and/or other suitable data) using a machine learning-based technique. Subsequently upon receiving a query, a set of features corresponding to the query, such as the length and/or frequency of the query, unigram probabilities of respective words and/or groups of words in the query, presence of pre-designated words or phrases in the query, or the like, can be generated. The generated features can then be analyzed in combination with the query prediction model to classify the query by predicting whether the query is aimed at a head Uniform Resource Locator (URL) or a tail URL. Based on this prediction, an appropriate index or combination of indexes can be assigned to answer the query. | 07-15-2010 |
20100179930 | Method and System for Developing Predictions from Disparate Data Sources Using Intelligent Processing - Provided herein is a platform for prediction based on extraction of features and observations collected from a large number of disparate data sources that uses machine learning to reinforce quality of collection, prediction and action based on those predictions. | 07-15-2010 |
20100179931 | DEVELOPING SYSTEM THINKERS - The system thinker application receives a first issue, a first resolution to the first issue, and a first plurality of skills. The system thinker application searches a system environment electronic profile for a second issue, a second resolution to the second issue, and a second plurality of skills, wherein the system environment electronic profile contains a plurality of component profiles, and wherein the plurality of component profiles contain a second issue, a second resolution to the second issue, and a second plurality of skills. The system thinker application determines if the first issue, the first resolution to the first issue, and any one of the first plurality of skills are similar to any one of the second issue, the second resolution to the second issue, and any one of the second plurality of skills. The system thinker application adds skills to the system environment electronic profile and the component profile. | 07-15-2010 |
20100179932 | ADAPTIVE DRIVE SUPPORTING APPARATUS AND METHOD - Provided are an adaptive drive supporting apparatus and method that provide a personalized telematics user interface capable of supporting safe driving and convenient use. The adaptive drive supporting apparatus includes: a statistics database unit which stores and manages information on an average degree of attention required when a driving operation, a state of a car, or an external environment changes, information on degrees of attention required for manipulations of interfaces of the car, and a similarity between the functions of the interfaces; a personal characteristic setting unit which sets an individual degree of attention for each driver based on the average degree of attention according to a change in at least one of the driving operation, the state of the car, and the external environment; and an interface providing unit which determines whether or not a sum of the individual degree of attention and the degree of attention required when each driver manipulates a requested interface is larger than a predetermined threshold degree of attention required for safe driving. | 07-15-2010 |
20100179933 | SUPERVISED SEMANTIC INDEXING AND ITS EXTENSIONS - A system and method for determining a similarity between a document and a query includes building a weight vector for each of a plurality of documents in a corpus of documents stored in memory and building a weight vector for a query input into a document retrieval system. A weight matrix is generated which distinguishes between relevant documents and lower ranked documents by comparing document/query tuples using a gradient step approach. A similarity score is determined between weight vectors of the query and documents in a corpus by determining a product of a document weight vector, a query weight vector and the weight matrix. | 07-15-2010 |
20100179934 | KERNEL-BASED METHOD AND APPARATUS FOR CLASSIFYING MATERIALS OR CHEMICALS AND FOR QUANTIFYING THE PROPERTIES OF MATERIALS OR CHEMICALS IN MIXTURES USING SPECTROSCOPIC DATA - A kernel-based method determines the similarity of a first spectrum and a second spectrum. Each spectrum represents a result of spectral analysis of a material or chemical and comprises a set of spectral attributes distributed across a spectral range. The method calculates a kernel function which makes use of the shape of the spectral response surrounding a spectral point. This is achieved by comparing the value of an spectral attribute in a spectrum and each of a set of neighbouring spectral attributes within a window around the spectral attribute. Weighting values can be applied to calculations when deriving the kernel function. The weighting values can assign different degrees of importance to different regions of the spectrum. The method can be used to: classify unknown spectra; predict the concentration of an analyte within a mixture; database searching for the closest match using a kernel-derived distance metric; visualisation of high-dimensional spectral data in two or three dimensions. | 07-15-2010 |
20100185567 | SUPERVISION BASED GROUPING OF PATTERNS IN HIERARCHICAL TEMPORAL MEMORY (HTM) - A HTM network that uses supervision signals such as indexes for correct categories of the input patterns to group the co-occurrences detected in the node. In the training mode, the supervised learning node receives the supervision signals in addition to the indexes or distributions from children nodes. The supervision signal is then used to assign the co-occurrences into groups. The groups include unique groups and nonunique groups. The co-occurrences in the unique group appear only when the input data represent certain category but not others. The nonunique groups include patterns that are shared by one or more categories. In an inference mode, the supervised learning node generates distributions over the groups created in the training mode. A top node of the HTM network generates an output based on the distributions generated by the supervised learning node. | 07-22-2010 |
20100185568 | Method and System for Document Classification - A system and method to classify web-based documents as articles or non-articles is disclosed. The method generates a machine learning model from a human labelled training set which contains articles and non-articles. The machine learning model is applied to new articles to label them as articles or non-articles. The method generates the machine learning model based on content, such as text and tags of the web-based documents. The invention also provides for devices which incorporate the machine learning model, allowing such devices to classify documents as articles or non-articles. | 07-22-2010 |
20100185569 | Smart Attribute Classification (SAC) for Online Reviews - Techniques for identifying attributes in a sentence and determining a number of attributes to be associated with the sentence is described. | 07-22-2010 |
20100185570 | THREE-DIMENSIONAL MOTION IDENTIFYING METHOD AND SYSTEM - A three-dimensional (3D) motion identifying method and system are used for identifying a motion of an object in a 3D space. First, the method provides a database recording sets of predetermined inertial information, and each of the sets of predetermined inertial information is an inertial movement of a specific motion in the 3D space. Then, inertial information of the object in moving is retrieved via a motion sensor in the object, and the inertial information is compared with all predetermined inertial information in the database to determine similarities therebetween. Finally, whether the motion of the object is the same as any predetermined inertial information or not is determined according to a degree of the similarity. As a result, more complicated motions of the object can be directly identified via the comparison with the database. | 07-22-2010 |
20100185571 | INFORMATION PROCESSING APPARATUS, INFORMATION PROCESSING METHOD, AND PROGRAM - An information processing apparatus includes a plurality of information input units inputting information including image information or sound information in a real space, an event detection unit analyzing input information from the information input units so as to generate event information including estimated position information and estimated identification information of users present in the real space, and an information integration processing unit setting hypothesis data regarding user existence and position information and user identification information of the users in the real space and updating and selecting hypothesis data based on the event information so as to generate analysis information including user existence and position information and user identification information of the users in the real space. | 07-22-2010 |
20100191679 | METHOD AND APPARATUS FOR CONSTRUCTING A CANONICAL REPRESENTATION - Some embodiments provide systems and techniques to facilitate construction of a canonical representation (CR) which represents a logical combination of a set of logical functions. During operation, the system can receive a CR-size limit. Next, the system can construct a set of CRs based on the set of logical functions, wherein each CR in the set of CRs represents a logical function in the set of logical functions. The system can then combine a subset of the set of CRs to obtain a combined CR. Next, the system can identify a problematic CR which when combined with the combined CR causes the CR-size limit to be exceeded. The system can then report the problematic CR and/or a logical function associated with the problematic CR to a user, thereby helping the user to identify an error in the set of logical functions. | 07-29-2010 |
20100191680 | METHOD AND APPARATUS FOR PREDICTING PREFERENCE RATING FOR CONTENT, AND METHOD AND APPARATUS FOR SELECTING SAMPLE CONTENT - Provided are a method and an apparatus for predicting a preference rating for content, and a method and an apparatus for selecting sample content in order to predict a preference rating for the content. In the method of predicting a preference rating for the content, a list of users having similar preferences to a target user is extracted from content usage information collected with respect to the same content, and the target user's preference rating for the content is predicted by applying preference rating information of the users with similar preferences to a machine learning algorithm. | 07-29-2010 |
20100191681 | Prognostics and health management method for aging systems - The present invention provides a novel prognostic and health management method for natural aging systems. This prognostic and health management method can detect anomalies in a system in advance, and can determine whether the detected anomalies are due to natural aging or other aging processes. In this prognostic method, a moving window method for improving the performance of the conventional data-driven prognostic methods is described. This prognostic and health management method combines with the detections by the data-driven prognostic method based on the conventional training and moving window methods to determine whether the detected anomalies are due to natural aging or other aging processes and in so doing can reduce the number of false alarms; reduce cost of a system by decreasing the unnecessary maintenance, downtime, and inventory; can extend the life of systems; and can assist in the design and qualification of future systems to improve their reliability. | 07-29-2010 |
20100191682 | Learning Apparatus, Learning Method, Information Processing Apparatus, Data Selection Method, Data Accumulation Method, Data Conversion Method and Program - There is provided a learning apparatus including: a first data acquisition unit which acquires first user preference data belonging to a first data space; a second data acquisition unit which acquires second user preference data of a user in common with the first user preference data, the second user preference data belonging to a second data space which is different from the first data space; a compression unit which generates first compressed user preference data having less data item number from the first user preference data by utilizing a first set of parameters; and a learning unit which learns a second set of parameters utilized for generating second compressed user preference data having the same data item number as that of the first compressed user preference data from the second user preference data so that difference between the first compressed user preference data and the second compressed user preference data is to be small across a plurality of users. | 07-29-2010 |
20100191683 | CONDENSED SVM - The present invent ion provides a condensed SVM for high-speed learning using a large amount of training data. A first stage WS selector samples a plurality of training data from a training data DB, selects an optimal training vector x | 07-29-2010 |
20100191684 | Trainable hierarchical memory system and method - Memory networks and methods are provided. Machine intelligence is achieved by a plurality of linked processor units in which child modules receive input data. The input data are processed to identify patterns and/or sequences. Data regarding the observed patterns and/or sequences are passed to a parent module which may receive as inputs data from one or more child modules. the parent module examines its input data for patterns and/or sequences and then provides feedback to the child module or modules regarding the parent-level patterns that correlate with the child-level patterns. These systems and methods are extensible to large networks of interconnected processor modules. | 07-29-2010 |
20100198756 | METHODS AND SYSTEMS FOR MATCHING RECORDS AND NORMALIZING NAMES - Methods and systems are provided for normalizing strings and for matching records. In one implementation, a string is tokenized into components. Sequences of tags are generated by assigning tags to the components. A sequence of states is determined based on the sequences of tags. A normalized string is generated by normalizing the sequence of the states. A key record including key fields is extracted from a first data source. A candidate record including candidate fields is extracted from a second data source. A numerical record including numerical fields is computed by comparing the key fields and the candidate fields using comparison functions. Matching functions determined by an additive logistic regression method are applied to the numerical fields. Whether the key record and the candidate record are a match is determined based on a sum of results of the matching functions. | 08-05-2010 |
20100198757 | PERFORMANCE OF A SOCIAL NETWORK - Providing for characterizing and determining effectiveness of social networks is described herein. By way of example, data descriptive of inter-relationships of persons can be employed to generate a social connectivity map for users of a communication network. Data disseminated or consumed via the communication network can be monitored and characterized in conjunction with task performance. The characterization can be compared with a performance benchmark to rate a composition of a social network, or underlying network applications and functions, in effecting user tasks or other user activities. Accordingly, individuals and organizations can determine and compare the effectiveness of a network in assisting user activities based on predetermined benchmarks, which can be tuned to various aspects, functions or applications of an underlying social network. | 08-05-2010 |
20100198758 | DATA CLASSIFICATION METHOD FOR UNKNOWN CLASSES - A system and method for creating a CD Tree for data having unknown classes are provided. Such a method can include dividing training data into a plurality of subsets of node training data at a plurality of nodes arranged in a hierarchical arrangement, wherein the node training data has a range. Furthermore, dividing node training data at each node can include, ordering the node training data, generating a plurality of separation points and a plurality of pairs of bins from the node training data, wherein each pair of bins includes a first bin and a second bin with a separation point being located between the first bin and the second bin, and classifying the node training data into either the first bin or the second bin for each of the separation points, wherein the classifying is based on a data classifier. Validation data can be utilized to calculate the bin accuracy between the node training data bin pairs and the validation data bin pairs for each separation point, and the separation point having a high bin accuracy can be selected as the node separation point. | 08-05-2010 |
20100198759 | Portal Performance Optimization - A method for portal performance optimization comprises receiving a request for a portal page, the portal page comprising a plurality of portlets; determining a current system load; determining, based on the current system load, whether a performance rule is triggered; and in the event a performance rule is triggered, deactivating at least one of the plurality of portlets. A system for portal performance optimization comprises a portal server configured to receive a request for a portal page, the portal page comprising a plurality of portlets, the portal server comprising a performance management component, the performance management component configured to determine a current system load; and a rules engine, the rules engine configured to determine if a performance rule is triggered by the determined current system load, and, in the event a performance rule is triggered, to apply the triggered performance rule to at least one of the plurality of portlets. | 08-05-2010 |
20100198760 | APPARATUS AND METHODS FOR MUSIC SIGNAL ANALYSIS - An apparatus for modelling layers in a music signal comprises a rhythm modelling module configured to model rhythm features of the music signal; a harmony modelling module configured to model harmony features of the music signal; and a music region modelling module configured to model music region features from the music signal. | 08-05-2010 |
20100198761 | SYSTEMS, METHODS AND CIRCUITS FOR LEARNING OF RELATION-BASED NETWORKS - Circuits, devices and methods for processing learning networks are implemented using a variety of methods and devices. One example involves a circuit-implemented method to identify a relationship of objects in a set of objects. Local scores are generated for the object and possible parents. The local scores indicate relationship strength between object and parent. The results are stored in a memory. A state-machine circuit is used to perform sampling and searching of the parent sets for each data node. The local scores are used to encode orderings of the parent. An algorithm is executed that uses the encoded possible orderings and a random variable to generate and score a current order and a proposed order of the possible parent sets. The proposed orders are accepted or rejected based on probability rules applied to the scores for the current and proposed orders. Structures are sampled to assess a Bayesian-based relationship. | 08-05-2010 |
20100198762 | AUTOMATED PREDICTIVE MODELING OF BUSINESS FUTURE EVENTS BASED ON HISTORICAL DATA - Predictive models are developed automatically for a plurality of modeling variables. The plurality of modeling variables is transformed, based on a transformation rule. A clustering of the transformed modeling variables is performed to create variable clusters. A set of variables is selected from the variable clusters based on a selection rule. A regression of the set of variables is performed to determine prediction variables. The prediction variables are utilized in developing a predictive model. The development of the predictive model may include modification of the predictive model, review of the plurality of transformations, and validation of the predictive model. | 08-05-2010 |
20100198763 | Reduction Of Classification Error Rates And Monitoring System Using An Artificial Class - Systems and methods for enhancing the accuracy of classifying a measurement by providing an artificial class. Seizure prediction systems may employ a classification system including an artificial class and a user interface for signaling uncertainty in classification when a measurement is classified in the artificial class. | 08-05-2010 |
20100205120 | PLATFORM FOR LEARNING BASED RECOGNITION RESEARCH - A method for researching and developing a recognition model in a computing environment, including gathering one or more data samples from one or more users in the computing environment into a training data set used for creating the recognition model, receiving one or more training parameters defining a feature extraction algorithm configured to analyze one or more features of the training data set, a classifier algorithm configured to associate the features to a template set, a selection of a subset of the training data set, a type of the data samples, or combinations thereof, creating the recognition model based on the training parameters, and evaluating the recognition model. | 08-12-2010 |
20100205121 | ASSOCIATIVE MEMORY LEARNING AGENT FOR ANALYSIS OF MANUFACTURING NON-CONFORMANCE APPLICATIONS - A system for assisting a user in determining a cause of a manufacturing non-conformance situation in a manufacturing application. The system may include an associative memory subsystem that is populated with a plurality of entity types, with each entity type including at least one entity, to form an associative memory. A user input device enables a user to input manufacturing non-conformance information into the associative memory subsystem that causes the associative memory subsystem to perform an initial search. The initial search generates a plurality of the entities that has a primary relevance useful for investigating the manufacturing non-conformance situation. An output device is responsive to the associative memory subsystem presents the plurality of entities found during the initial search to the user. | 08-12-2010 |
20100205122 | METHODS AND SYSTEMS OF ADAPTIVE COALITION OF COGNITIVE AGENTS - Coalitions from interactions and adaptations of cognitive map agents are evolved using an algorithm. A population of agents are seeded with cognitive map variants characterizing different cultures or different affiliations. The algorithm evolves this population by modifying the cognitive maps using a modified Particle Swarm Optimization algorithm. The modifications include modification to weights of the cognitive map, and the structure of the cognitive map of the global best (gbest) in the neighborhood is imitated according to a weighted random selection, based on the commonality of the node characteristic in the neighborhood. The end results indicate whether a coalition is possible and what cognitive maps emerge. These results are visualized on a 2D grid and measured with a clustering metric. | 08-12-2010 |
20100205123 | SYSTEMS AND METHODS FOR IDENTIFYING UNWANTED OR HARMFUL ELECTRONIC TEXT - The present invention relates to systems and methods for identifying and removing unwanted or harmful electronic text (e.g., spam). In particular, the present invention provides systems and methods utilizing inexact string matching methods and machine learning and non-learning methods for identifying and removing unwanted or harmful electronic text. | 08-12-2010 |
20100205124 | SUPPORT VECTOR MACHINE-BASED METHOD FOR ANALYSIS OF SPECTRAL DATA - Support vector machines are used to classify data contained within a structured dataset such as a plurality of signals generated by a spectral analyzer. The signals are pre-processed to ensure alignment of peaks across the spectra. Similarity measures are constructed to provide a basis for comparison of pairs of samples of the signal. A support vector machine is trained to discriminate between different classes of the samples. to identify the most predictive features within the spectra. In a preferred embodiment feature selection is performed to reduce the number of features that must be considered. | 08-12-2010 |
20100205125 | IDENTIFYING INVENTION FEATURE PERMUTATIONS FOR A REASONABLE NUMBER OF PATENT APPLICATION CLAIMS - Permutations of features of an invention are ranked in accordance with factors such as importance and specificity to identify a reasonable number of (i.e. 20 or fewer) permutations as candidates for structuring a corresponding number of claims for a patent application. The identified permutations desirably include permutations corresponding to claims of broad scope, claims of narrow scope, and claims of intermediate scope; and exclude illogical or impractical permutations of features. | 08-12-2010 |
20100211533 | EXTRACTING STRUCTURED DATA FROM WEB FORUMS - The web forum data extraction technique is designed for the structured data extraction of data on web forums using both page-level information and site-level knowledge. To do this, the technique finds the kinds of page objects a forum site has, which object a page belongs to, and how different page objects are connected with each other. This information can be obtained by re-constructing the sitemap of the target forum which is based on a Data Object Model of the target forum. The web forum data extraction technique collects three kinds of evidence for data extraction: 1) inner-page features which cover both semantic and layout information on an individual page; 2) inter-vertex features which describe linkage-related observations; and 3) inner-vertex features which characterize interrelationships among pages in one vertex. The technique employs Markov Logic Networks to combine the types of evidence statistically for inference and thereby can extract the desired structures. | 08-19-2010 |
20100211534 | Efficient computation of ontology affinity matrices - In one embodiment, generating an ontology includes accessing an inverted index comprising a plurality of inverted index lists. An inverted index list may correspond to a term of a language. Each inverted index list may comprise a term identifier of the term and one or more document identifiers indicating one or more documents of a document set in which the term appears. The embodiment also includes generating a term identifier index according to the inverted index. The term identifier index comprises a plurality of sections and each section corresponds to a document. Each section may comprise one or more term identifiers of one or more terms that appear in the document. | 08-19-2010 |
20100217730 | TEMPORALLY-CONTROLLED ITEM RECOMMENDATION METHOD AND SYSTEM BASED ON RATING PREDICTION - The present invention proposes a temporally-controlled item recommendation method and system based on rating prediction. According to this invention, the item recommendation method comprises inputting an item to be recommended; determining a temporal rating model related to the item, the temporal rating model being used to predict variation of the rating of the item with time; applying one or more recommendation strategies to the determined temporal rating model to determine optimal recommendation times of the item; and recommending the item to a user at the determined optimal recommendation times. In different embodiments, the temporal rating model of the item can be selected from a set of pre-stored temporal rating models or automatically generated according to history data in the system. In addition, the selected temporal rating model can be adjusted in accordance with user preference information or user feedback information. The item recommendation system of this invention is able to consider the change of a user's interest in a given item with time so as to increase the effectiveness of recommendations and improve user experience. | 08-26-2010 |
20100223212 | TASK-RELATED ELECTRONIC COACHING - Providing for task-related electronic feedback based on user interaction with a communication network is described herein. By way of example, user interactions the network or a network interface can be monitored to identify user activities performed in conjunction with a task. A rating for performance of the task can be obtained via comparison of user activities with benchmark performance activities. Based on the rating and user-benchmark comparison, inefficiencies can be identified, along with corrective actions for such activities. The corrective actions can then be output to coach the user on techniques for improving performance of the task. Accordingly, by employing corrective feedback based on monitored user activity, personal training can be automated, potentially reducing time and cost of such training. | 09-02-2010 |
20100223213 | SYSTEM AND METHOD FOR PARALLELIZATION OF MACHINE LEARNING COMPUTING CODE - Systems and methods for parallelization of machine learning computing code are described herein. In one aspect, embodiments of the present disclosure include a method of generating a plurality of instruction sets from machine learning computing code for parallel execution in a multi-processor environment, which may be implemented on a system, of, partitioning training data into two or more training data sets for performing machine learning, identifying a set of concurrently-executable tasks from the machine learning computing code, assigning the set of tasks to two or more of the computing elements in the multi-processor environment, and/or generating the plurality of instruction sets to be executed in the multi-processor environment to perform a set of processes represented by the machine learning computing code. | 09-02-2010 |
20100223214 | AUTOMATIC EXTRACTION USING MACHINE LEARNING BASED ROBUST STRUCTURAL EXTRACTORS - A method and apparatus for automatically extracting information from a large number of documents through applying machine learning techniques and exploiting structural similarities among documents. A machine learning model is trained to have at least 50% accuracy. The trained machine learning model is used to identify information attributes in a sample of pages from a cluster of structurally similar documents. A structure-specific model of the cluster is created by compiling a list of top-K locations for each attribute identified by the trained machine learning model in the sample. These top-K lists are used to extract information from the pages of the cluster from which the sample of pages was taken. | 09-02-2010 |
20100223215 | SYSTEMS AND METHODS OF MAKING CONTENT-BASED DEMOGRAPHICS PREDICTIONS FOR WEBSITES - Systems and methods for making demographic predictions for websites and web-pages. Embodiments include a system and a method of making demographic predictions for websites. The system and method select one or more websites with known demographic attributes for use as training websites, obtain demographic attributes data of the training websites, determine first features of web-pages of the training websites and develop a prediction model using the determined first features and the obtained demographic attributes data. The prediction model predicts one or more values for a target demographic attribute. The system and method determine second features of web-pages of a target website and apply the prediction model to the determined second features of the target website to predict one or more values for the target demographic attribute of the target website. | 09-02-2010 |
20100223216 | ARTIFICIAL VISION SYSTEM AND METHOD FOR KNOWLEDGE-BASED SELECTIVE VISUAL ANALYSIS - Generally the background of the present invention is the field of artificial vision systems, i.e. systems having a visual sensing means (e.g. a video camera) and a following processing stage implemented using a computing unit. The processing stage outputs a representation of the visually analysed scene, which output can then be fed to control different actors, such as e.g. parts of a vehicle (automobile, plane, . . . ) or a robot, preferably an autonomous robot such as e.g. a humanoid robot. | 09-02-2010 |
20100228691 | Media Tag Recommendation Technologies - Technologies for recommending relevant tags for the tagging of media based on one or more initial tags provided for the media and based on a large quantity of other tagged media. Sample media as candidates for recommendation are provided by a set of weak rankers based on corresponding relevance measures in semantic and visual domains. The various samples provided by the weak rankers are then ranked based on relative order to provide a list of recommended tags for the media. The weak rankers provide sample tags based on relevance measures including tag co-occurrence, tag content correlation, and image-conditioned tag correlation. | 09-09-2010 |
20100228692 | SYSTEM AND METHOD FOR MULTI-MODAL BIOMETRICS - A system and method relate to multi-modal biometrics. A single modality score is generated for each of a plurality of biometric modalities. A classifier is selected from a database of multi-modal classifiers, and a multi-modal fusion is applied to the single modality scores using the classifier. The single modality scores are then aggregated. A context dependent model is generated, and a measure of the context in which the biometric samples were obtained is applied to the aggregated single modality scores. It is then determined whether there is a match between two or more biometric samples. | 09-09-2010 |
20100228693 | METHOD AND SYSTEM FOR GENERATING A DOCUMENT REPRESENTATION - A method, system and computer program product for generating a document representation are disclosed. The system includes a server and a client computer, and the method involves: receiving into memory a resource containing at least one sentence of text; producing a tree comprising tree elements indicating parts-of-speech and grammatical relations between the tree elements; producing semantic structures each having three tree elements to represent a simple clause (subject-predicate-object); and storing a semantic network of semantic structures and connections therebetween. The semantic network may be created from a user provided root concept. Output representations include concept maps, facts listings, text summaries, tag clouds, indices; and an annotated text. The system interactively modifies semantic networks in response to user feedback, and produces personal semantic networks and document use histories. | 09-09-2010 |
20100235305 | SYSTEM AND METHOD OF ON-DEMAND DOCUMENT PROCESSING - A document processing method includes receiving, at a server with a network interface, electronic documents from a user. The server includes a software application adapted to recognize a class of electronic documents to which the electronic documents belong. The method also includes processing the electronic documents received from the user to extract data therefrom based on a recognition that the electronic documents belong to the class of electronic documents. The extracted data corresponds to a service being provided to the user. The method also includes automatically mapping the extracted data from the processed electronic documents to a data repository on the server. The data repository is accessible by the user through the network interface. The method also includes electronically generating output data based on the mapped data from the data repository to the user. The output data corresponds to the service being provided to the user. | 09-16-2010 |
20100235306 | ADAPTIVE TIMELOG SYSTEM - An adaptive time log system that includes computer based systems and methods for monitoring, recording categorizing and reporting user activity on a timeline basis is provided. | 09-16-2010 |
20100235307 | METHOD, SYSTEM, AND COMPUTER PROGRAM FOR USER-DRIVEN DYNAMIC GENERATION OF SEMANTIC NETWORKS AND MEDIA SYNTHESIS - This invention relates generally to classification systems. More particularly this invention relates to a system, method, and computer program to dynamically generate a domain of information synthesized by a classification system or semantic network. The invention discloses a method, system, and computer program providing a means by which an information store comprised of knowledge representations, such as a web site comprised of a plurality of web pages or a database comprised of a plurality of data instances, may be optimally organized and accessed based on relational links between ideas defined by one or more thoughts identified by an agent and one or more ideas embodied by the data instances. Such means is hereinafter referred to as a “thought network”. | 09-16-2010 |
20100235308 | TEXT ANALYSIS DEVICE AND METHOD AND PROGRAM - A text analysis device includes a storage unit configured to store opinions of users who participate in a discussion about a predetermined theme as text data and author information for specifying authors of the text data, a feature quantity data generation unit configured to generate feature quantity data of the text data stored in the storage unit, an observation time-series signal generation unit configured to generate observation time-series signals based on information obtained by performing a predetermined process with respect to the feature quantity data, a change point detection unit configured to detect a change point of the discussion based on the observation time-series signals, and an influence specifying unit configured to specify an opinion having influence on an opinion corresponding to the specified text data out of opinions of the discussion based on the detected change point and the author information. | 09-16-2010 |
20100241597 | DYNAMIC ESTIMATION OF THE POPULARITY OF WEB CONTENT - Techniques are presented for estimating the current popularity of web content. Click and view data for articles are used to estimate popularity of the articles by analyzing click-through rates. Click-though rates are estimated such that a current click-through rate reflects fluctuations in popularity of articles through time. | 09-23-2010 |
20100241598 | METHOD, PROGRAM, AND APPARATUS FOR GENERATING TWO-CLASS CLASSIFICATION/PREDICTION MODEL - A two-class classification/prediction model is generated in a simple operation by performing two-class classification with a classification rate substantially close to 100%. The two-class classification/prediction model is generated by a) obtaining a discriminant function for classifying a training sample set into two predetermined classes on the basis of an explanatory variable generated for each sample contained in the training sample set, b) calculating a discriminant score for each training sample by using the obtained discriminant function, c) determining, based on the calculated discriminant score, whether the training sample is correctly classified or not, d) determining a misclassified-sample region based on maximum and minimum discriminant scores taken from among misclassified samples in the training sample set, e) constructing a new training sample set by extracting the training samples contained in the misclassified-sample region, and f) repeating a) to e) for the new training sample set. | 09-23-2010 |
20100250472 | SYSTEM AND METHOD OF MACHINE-AIDED INFORMATION EXTRACTION RULE DEVELOPMENT - An automatic rule generation system generates rules for fact extraction. A rule generation module receives a sample and generates a rule from the sample. A rule relaxation module generates a relaxed rule from the rule. A rule testing module generates a reverse index from a corpus, applies the relaxed rule to the reverse index, and generates text segments. An information extraction module generates modified text segments from the relaxed rule and the text segments. A candidate suggestion module performs a candidate generation process: if the candidate generation process generates no candidates, the candidate suggestion module signals the rule relaxation module to generate a further relaxed rule to use as the relaxed rule. A user evaluates a candidate and provides the candidate as an additional sample for the automatic rule generation system to generate another rule to use as the rule. As a result of performing these actions iteratively, the rule is eventually generated and relaxed to result in an appropriate rule to use for fact extraction. | 09-30-2010 |
20100250473 | Active Learning Method for Multi-Class Classifiers - A method trains a multi-class classifier by iteratively performing the following steps until a termination condition is reached. The probabilities of class membership for unlabeled data obtained from an active pool of unlabeled data are estimated. A difference between a largest probability and a second largest probability is determined. The unlabeled data with the lowest difference is selected, labeled and then added to a training data set for training the classifier. | 09-30-2010 |
20100250474 | PREDICTIVE CODING OF DOCUMENTS IN AN ELECTRONIC DISCOVERY SYSTEM - Embodiments of the invention relate to systems, methods, and computer program products for improved electronic discovery. More specifically, embodiments relate to computer program products for predictive and automated coding of identical or highly similar documents for the purpose of limiting the volume of documents requiring review and thereby increasing the overall efficiency of the document review process. | 09-30-2010 |
20100262568 | Scalable Clustering - A scalable clustering system is described. In an embodiment the clustering system is operable for extremely large scale applications where millions of items having tens of millions of features are clustered. In an embodiment the clustering system uses a probabilistic cluster model which models uncertainty in the data set where the data set may be for example, advertisements which are subscribed to keywords, text documents containing text keywords, images having associated features or other items. In an embodiment the clustering system is used to generate additional features for associating with a given item. For example, additional keywords are suggested which an advertiser may like to subscribe to. The additional features that are generated have associated probability values which may be used to rank those features in some embodiments. User feedback about the generated features is received and used to revise the feature generation process in some examples. | 10-14-2010 |
20100262569 | DATA CONVERTING APPARATUS AND MEDIUM HAVING DATA CONVERTING PROGRAM - A data converting apparatus and a data converting program are suitable to quantitatively estimate, based on a temporal alteration of a stimulus value, a temporal alteration of a sensitivity brought to human beings. The data converting apparatus includes a decomposing unit subjecting temporal alteration data of a stimulus value to wavelet decomposition to extract plural time-frequency components contained in the temporal alteration data, a weighting unit weighting the plural extracted time-frequency components weighting coefficients which are predetermined based on a relationship between a temporal alteration of a stimulus value and a temporal alteration of a sensitivity of human beings to the stimulus value, and a synthesizing unit subjecting the plural weighted time-frequency components to wavelet synthesis to estimate a sensitivity brought to human beings when the stimulus value is subjected to temporal alteration according to the temporal alteration data. | 10-14-2010 |
20100262570 | Information Processing Apparatus and Method, and Program Thereof - There is provided an information processing apparatus including: evaluation information extracting means extracting evaluation information from evaluation of every user for an item; preference information creating means for creating preference information indicating a preference of every user on the basis of the evaluation information extracted by the evaluation information extracting means and an item characteristic amount indicating a characteristic of the item; space creating means for creating a space in which the user is located, according to the preference information; and display control means for controlling display of the user located in the space, according to the space created by the space creating means and the preference information. The apparatus may be applied to, for example, an image display apparatus which displays server images for providing a variety of items and information. | 10-14-2010 |
20100262571 | SYSTEMS AND METHODS FOR ORGANIZING DATA SETS - A method is provided for organizing data sets. In use, an automatic decision system is created or updated for determining whether data elements fit a predefined organization or not, where the decision system is based on a set of preorganized data elements. A plurality of data elements is organized using the decision system. At least one organized data element is selected for output to a user based on a score or confidence from the decision system for the at least one organized data element. Additionally, at least a portion of the at least one organized data element is output to the user. A response is received from the user comprising at least one of a confirmation, modification, and a negation of the organization of the at least one organized data element. The automatic decision system is recreated or updated based on the user response. Other embodiments are also presented. | 10-14-2010 |
20100268673 | ASSOCIATE MEMORY LEARNING AGENT TECHNOLOGY FOR TRAVEL OPTIMIZATION AND MONITORING - A method for assisting with evaluating travel related information. The method may involve defining a plurality of entity types for categorizing different types of travel related information. A data mining tool may be used to search at least one database for stored travel related information, and to denote specific items of the travel related information as entities. The data mining tool may be used to populate an associative learning memory with the entities and to store the entities in the associative learning memory. An entity analytics engine may be used to assist a user in searching the associative learning memory for specific ones of the entities that are at least one of identical or similar to specific travel related information provided by the user. Retrieved ones of the entities may be displayed for evaluation by the user. | 10-21-2010 |
20100268674 | Systems and Methods for Achieving PLMN Continuity When Moving Between Networks of Different Types Through Network Selection - Systems and methods for achieving PLMN continuity when moving between networks of different types through network selection are provided. When a mobile station moves from a first network type, such as cellular, to a second network type, such as GAN, if there is a PLMN discontinuity, this may result in a dropped call. In order to avoid this, networks for the first network type and the second network type are selected such that there is PLMN continuity. This can involve reselection of a different cellular network than one currently providing service to the mobile station. | 10-21-2010 |
20100274744 | System And Computer-Implemented Method For Generating Temporal Footprints To Identify Tasks - A system and computer-implemented method for generating temporal footprints to identify tasks is provided. One or more events performed by a user during execution of a task is recorded. Patterns including sequences of two or more of the events are identified. Each pattern occurs at a plurality of occurrences. A determination of whether each pattern is significant is made. A temporal distance between the events in each pattern occurrence for each pattern is identified. A pattern value is determined for each pattern based on a number of occurrences and the associated temporal distance. The pattern value is applied to a significance level. At least one of the patterns is determined to be significant when the pattern value satisfies the significance level. A temporal footprint is generated for the executed task and includes the significant patterns. | 10-28-2010 |
20100274745 | PREDICTION METHOD FOR MONITORING PERFORMANCE OF POWER PLANT INSTRUMENTS - Disclosed is a prediction method for monitoring performance of power plant instruments. The prediction method extracts a principal component of an instrument signal, obtains an optimized constant of a SVR model through a response surface methodology using data for optimization, and trains a model using training data. Therefore, compared to an existing Kernel regression method, accuracy for calculating a prediction value can be improved. | 10-28-2010 |
20100280978 | System and method for utility usage, monitoring and management - Systems and methods are provided for collecting waveform data for a plurality of appliances that may be found in a residential or commercial setting using multi-port outlet monitoring devices to obtain power consumption profiles that indicate power consumption on a per-appliance and/or per-location basis and/or per user basis. The plurality of appliances is reliably identified from the power consumption profiles. In accordance with a method embodiment, waveform data transmitted from an unknown appliance is independently metered via a multi-port monitoring device over an elapsed time period. The metered waveform data is wirelessly transmitted from the multi-port monitoring device to a co-located system controller which constructs an appliance signature. The process may be repeated to generate multiple appliance signatures. The one or more appliance signatures are compared to a database of pre-stored canonical signatures to determine if there is a match to identify the appliance. | 11-04-2010 |
20100280979 | MACHINE LEARNING HYPERPARAMETER ESTIMATION - A method of determining hyperparameters (HP) of a classifier ( | 11-04-2010 |
20100280980 | System and Method for Resolving Gamma Ray Spectra - A system for identifying radionuclide emissions is described. The system includes at least one processor for processing output signals from a radionuclide detecting device, at least one training algorithm run by the at least one processor for analyzing data derived from at least one set of known sample data from the output signals, at least one classification algorithm derived from the training algorithm for classifying unknown sample data, wherein the at least one training algorithm analyzes the at least one sample data set to derive at least one rule used by said classification algorithm for identifying at least one radionuclide emission detected by the detecting device. | 11-04-2010 |
20100287124 | HIERARCHICAL TEMPORAL MEMORY UTILIZING NANOTECHNOLOGY - Methods and systems are presented for constructing biological-scale hierarchically structured cortical statistical memory systems using currently available fabrication technology and meta-stable switching devices. Learning content-addressable memory and statistical random access memory circuits are detailed. Additionally, local and global signal modulation of bottom-up and top-down processing for the initiation and direction of behavior is disclosed. | 11-11-2010 |
20100287125 | INFORMATION PROCESSING UNIT, INFORMATION PROCESSING METHOD, AND PROGRAM - The present invention relates to an information processing unit, an information processing method, and a program that can allow two-class classification to be correctly performed based on the outputs from two or more classifiers. | 11-11-2010 |
20100287126 | BATTERY LEARNING SYSTEM - A fuel battery system is comprised of a power source circuit, a rotating electrical machine that is a load, a memory device and a control unit. Here, a battery learning system corresponds to an arrangement including a fuel battery that is a structural component of the power source circuit, a high frequency signal source, an electric current detection means, a voltage detection means, the memory device and a battery learning part that is a structural element of the control unit. An impedance value can be obtained from alternating current components of respective detecting values of the electric current detection means and the voltage detection means. The battery learning unit has an I-V characteristic curve learning module that learns an I-V characteristic curve and a learning prohibition judgment module that judges whether or not an acquiring interval of the impedance value is over a predetermined threshold interval set in advance, and prohibits learning if the former is over the latter. | 11-11-2010 |
20100287127 | SELF-LEARNING ENGINE FOR THE REFINEMENT AND OPTIMIZATION OF SURGICAL SETTINGS - The present invention pertains to a system (or engine) that monitors a system's performance during a surgery, analyzes that performance, and makes recommendations to the user/surgeon for changes in his settings and/or programs that will result in more effective and time-efficient surgeries. Further, the system may comprise one or more components, including, but not limited to, a user preference filter, a surgical circumstances filter, a surgical instrument, a real time data collection module, and an analysis module. | 11-11-2010 |
20100287128 | Anomaly Detection for Link-State Routing Protocols - Disclosed herein is an anomaly detection method for link-state routing protocols, a link-state routing protocol providing for link-state update (LSU) messages to be exchanged between nodes in a packet-based network, wherein each link-state update message includes link-state advertisement (LSA) message(s) each having a respective header. The method comprises monitoring the link-state advertisement messages exchanged in the network, extracting and forming respective feature vectors with the values in the fields of the headers of the monitored link-state advertisement messages, and detecting an anomaly related to routing based on the feature vectors. In particular, detecting an anomaly related to routing includes feeding the feature vectors to a machine learning system, conveniently a one-class classifier, preferably a one-class support vector machine (OC-SVM). | 11-11-2010 |
20100293115 | METHOD, SYSTEM AND APPARATUS FOR REAL-TIME CLASSIFICATION OF MUSCLE SIGNALS FROM SELF -SELECTED INTENTIONAL MOVEMENTS - A new method, system and apparatus is provided that enables muscle signals that correspond to muscle contractions to be mapped to one or more functions of an electronic device such as a prosthetic device or gaming apparatus. Muscle signals are classified in real-time from self-selected intentional movements. A self-training protocol allows users to select and label their own muscle contractions, and is operable to automatically determine the discernible and repeatable muscle signals generated by the user. A visual display means is used to provide visual feedback to users illustrating the responsiveness of the system to muscle signals generated by the user. | 11-18-2010 |
20100293116 | URL AND ANCHOR TEXT ANALYSIS FOR FOCUSED CRAWLING - Systems and methods of URL and anchor text analysis for focused crawling are disclosed. In an exemplary embodiment, a method may include training a focused crawler by: obtaining a training set of at least URL's or anchor text for a website, computing a score for the training set, and extracting a plurality of features of the training set, and computing a score for each of the plurality of features. The features identify key information contained in the website. The method may also include executing a trained focused crawler on other websites. | 11-18-2010 |
20100293117 | METHOD AND SYSTEM FOR FACILITATING BATCH MODE ACTIVE LEARNING - A method and system for performing batch mode active learning to train a classifier. According to embodiments of the present invention, unlabeled documents are selected from a corpus based on rewards associated with each unlabeled document. The reward is an indication of the increase to the accuracy of a classifier which may result if the document is used to train the classifier. When calculating a given reward, embodiments of the present invention address the uncertainty and diversity of a given document. Embodiments of the present invention reduce the resources utilized to perform classifier training. | 11-18-2010 |
20100293118 | METHODS AND SYSTEMS FOR PREDICTING PROTEIN-LIGAND COUPLING SPECIFICITIES - The invention provides methods and systems for predicting or evaluating protein-ligand coupling specificities. A pattern recognition model can be trained by selected sequence segments of training proteins which have a specified ligand coupling specificity. Each selected sequence segment is believed to include amino acid residue(s) that may contribute to the ligand coupling specificity of the corresponding training protein. Sequence segments in a protein of interest can be similarly selected and used to query the trained model to determine if the protein of interest has the same ligand coupling specificity as the training proteins. In one embodiment, the pattern recognition model employed is a hidden Markov model which is trained by concatenated cytosolic domains of GPCRs which have interaction preference to a specified class of G proteins. This trained model can be used to evaluate G protein coupling specificity of orphan GPCRs. | 11-18-2010 |
20100299287 | Monitoring time-varying network streams using state-space models - In one embodiment, a statistical model is generated based on observed data, the observed data being associated with a network device, online parameter fitting is performed on parameters of the statistical model, and for each newly observed data value, a forecast value is generated based on the statistical model, the forecast value being a prediction of a next observed data value, a forecasting error is generated based on the forecast value and the newly observed data value, and whether the data of the network stream is abnormal is determined based on a log likelihood ratio test of the forecasting errors and a threshold value. | 11-25-2010 |
20100299288 | RULE-BASED VOCABULARY ASSIGNMENT OF TERMS TO CONCEPTS - Methods and systems are described that involve rule-based vocabulary assignment of terms to concepts. Instead of assigning individual terms to each concept in a conceptualization of a domain, such as taxonomy, ontology, and so on, production rules are defined and assigned to each concept. The production rules produce at least one term to name a concept by referring to semantically related concepts to this concept. The production rules may include context information specifying the context where a given rule is valid. The methods and systems can be used to improve search capabilities for entities by enabling easier annotation of large conceptualizations. Further, the methods and systems can improve user experience by allowing context specific naming of entities. | 11-25-2010 |
20100299289 | SYSTEM AND METHOD FOR OBTAINING INFORMATION ABOUT BIOLOGICAL NETWORKS USING A LOGIC BASED APPROACH - A system and method of obtaining information concerning the structure-function relationship of biological networks can be studied holistically through the ensemble characterization of all the networks that realize a given biological function. A logic-based approach enables significant advances in computability and concept development (minimality and reducibility). The approach is applied to a biologically relevant trajectory and reveals some interesting properties. By using the approach, a cell cycle network is decomposed into three components with the functioning of each component explained. | 11-25-2010 |
20100299290 | Web Query Classification - A query phrase may be automatically classified to one or more topics of interest (e.g., categories) to assist in routing the query phrase to one or more appropriate backend databases. A selectional preference query classification technique may be used to classify the query phrase based on a comparison between the query phrase and patterns of query phrases. Additionally, or alternatively, a combination of query classification techniques may be used to classify the query phrase. Topical classification of a query phrase also may be used to assist a search system in delivering auxiliary information to a user who entered the query phrase. Advertisements, for instance, may be tailored based on classification rather than query keywords. | 11-25-2010 |
20100306138 | BEHAVIOR MONITORING SYSTEM AND METHOD - A computer-implemented behavior monitoring method is provided. The method includes receiving from a plurality of contributors a plurality of personal value preference indications. Either or both of location information corresponding to a determined location of a user device and communication information corresponding to a determined communication activity of the user device are received. Either or both of the location information and the communication information of the user device are compared with the plurality of personal value preference indications from the plurality of contributors. A behavior rating is determined based on the comparison of the location information and the communication information of the user device with the plurality of personal value preference indications, and the behavior rating is transmitted to a user. | 12-02-2010 |
20100306139 | CJK NAME DETECTION - Aspects directed to name detection are provided. A method includes generating a raw name detection model using a collection of family names and an annotated corpus including a collection of n-grams, each n-gram having a corresponding probability of occurring. The method includes applying the raw name detection model to a collection of semi-structured data to form annotated semi?structured data identifying n-grams identifying names and n?grams not identifying names and applying the raw name detection model to a large unannotated corpus to form a large annotated corpus data identifying n-grams of the large unannotated corpus identifying names and n-grams not identifying names. The method includes generating a name detection model, including deriving a name model using the annotated semi-structured data identifying names and the large annotated corpus data identifying names, deriving a not-name model using the semi?structured data not identifying names, and deriving a language model using the large annotated corpus. | 12-02-2010 |
20100306140 | COMBINATION CONTAMINANT SIZE AND NATURE SENSING SYSTEM AND METHOD FOR DIAGNOSING CONTAMINATION ISSUES IN FLUIDS - Systems and methods used to monitor a fluid where it is important to know the size, concentration and nature of particulates in the fluid. For example, the systems and method can be used to diagnose contamination issues in fluids such as fuel, lubrication, power transfer, heat exchange or other fluids in fluid systems, for example diesel engines or hydraulic systems, where contaminant particles in the fluids are of concern. | 12-02-2010 |
20100312725 | SYSTEM AND METHOD FOR ASSISTED DOCUMENT REVIEW - A system and method for reviewing documents are provided. A collection of documents is portioned into sets of documents for review by a plurality of reviewers. For each set, documents in the set are displayed on a display device for review by a reviewer and temporarily organized through grouping and sorting. The reviewer's labels for the displayed documents are received. Based on the reviewer's labels, a class from a plurality of classes is assigned to each of the reviewed documents. A classifier model stored in computer memory is progressively trained, based on features extracted from the reviewed documents in the set and their assigned classes. Prior to review of all documents in the set, a calculated subset of documents for which the classifier model assigns a class different from the one assigned based on the reviewer's label is returned for a second review by a reviewer. Models generated from one or more other document sets can be used to assess the review of a first of the sets. | 12-09-2010 |
20100312726 | FEATURE VECTOR CLUSTERING - One goal of computer services (e.g., email, web pages, blogs, advertisements, etc.) is to provide a user with Kinds (digital representations of everyday things) that may be relevant and interesting to the user. Users and Kinds may be plotted within a multidimensional matrix as feature vectors based upon their respective characteristics. An unsupervised clustering technique may be executed upon the matrix to create a mathematical cluster of feature vectors having similar characteristics. For example, a clothing cluster may comprise a dress Kind, a shoe Kind, a wool Kind, a watch Kind, etc. because the unsupervised clustering technique may determine these Kinds are plotted within the matrix in such a way that they have similar characteristics relating to clothing. The unsupervised clustering technique may also be utilized in determining which Kinds may be relevant to a user given a particular context with which a user is engaged with a computer resource. | 12-09-2010 |
20100318477 | FAST AND EFFICIENT NONLINEAR CLASSIFIER GENERATED FROM A TRAINED LINEAR CLASSIFIER - A classifier method comprises: projecting a set of training vectors in a vector space to a comparison space defined by a set of reference vectors using a comparison function to generate a corresponding set of projected training vectors in the comparison space; training a linear classifier on the set of projected training vectors to generate a trained linear classifier operative in the comparison space; and transforming the trained linear classifier operative in the comparison space into a trained nonlinear classifier that is operative in the vector space to classify an input vector. | 12-16-2010 |
20100318478 | INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING METHOD, AND PROGRAM - An information processing device includes: a calculating unit configured to calculate a current-state series candidate that is a state series for an agent capable of actions reaching the current state, based on a state transition probability model obtained by performing learning of the state transition probability model stipulated by a state transition probability that a state will be transitioned according to each of actions performed by an agent capable of actions, and an observation probability that a predetermined observation value will be observed from the state, using an action performed by the agent, and an observation value observed at the agent when the agent performs an action; and a determining unit configured to determine an action to be performed next by the agent using the current-state series candidate in accordance with a predetermined strategy. | 12-16-2010 |
20100318479 | INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING METHOD, AND PROGRAM - An information processing device includes: a learning section configured to learn a state transition probability model defined by state transition probability for each action of a state making a state transition due to an action performed by an agent capable of performing action and observation probability of a predetermined observed value being observed from the state, using an action performed by the agent and an observed value observed in the agent when the agent has performed the action. | 12-16-2010 |
20100318480 | LEARNING CONTROL SYSTEM AND LEARNING CONTROL METHOD - A learning control system according to the present invention is one which performs learning of action values of actions in an apparatus which identifies its state as one of predetermined states, and selects an action based on the obtained action values and the identified state. The learning control system includes n action value learning devices including the first to the n th learning devices which perform learning of n action values from Q | 12-16-2010 |
20100318481 | Generating Test Data - Generating test data includes: reading values occurring in at least one field of multiple records from a data source; storing profile information including statistics characterizing the values; generating a model of a probability distribution for the field based on the statistics; generating multiple test data values using the generated model such that a frequency at which a given value occurs in the test data values corresponds to a probability assigned to that given value by the model; and storing a collection of test data including the test data values in a data storage system. | 12-16-2010 |
20100318482 | Kernels for Identifying Patterns in Datasets Containing Noise or Transformation Invariances - Learning machines, such as support vector machines, are used to analyze datasets to recognize patterns within the dataset using kernels that are selected according to the nature of the data to be analyzed. Where the datasets include an invariance transformation or noise, tangent vectors are defined to identify relationships between the invariance or noise and the training data points. A covariance matrix is formed using the tangent vectors, then used in generation of the kernel, which may be based on a kernel PCA map. | 12-16-2010 |
20100325070 | Isolating Changes in Dynamic Systems - A software optimization system isolates an effect of a change in a control variable from effects of ongoing, unknown changes in other variables. The system discards effects due to noise so that effects of interest to a programmer are more easily visible. The software optimization system treats variations in one or more control variables and in the output of the system as signals. The system varies the control variable at a specific frequency unlikely to correlate with uncontrolled variations in external variables. The system uses digital signal processing (DSP) techniques to filter the output, isolating the frequency of the control variable variation. The system then compares the resulting filtered output to the input to determine the approximate effect of the variation in the control variable. | 12-23-2010 |
20100325071 | SYSTEM AND METHOD FOR EMPIRICAL ENSEMBLE-BASED VIRTUAL SENSING OF GAS EMISSION - An empirical ensemble based virtual sensor system (VS) for the estimation of an amount of a gas (G) resulting from a combustion process (CP) comprising two or more empirical models (NN | 12-23-2010 |
20100325072 | SYSTEM AND METHOD FOR SOLVING MULTIOBJECTIVE OPTIMIZATION PROBLEMS - A system and method for solving a set of optimization problems initializes a current region of optimal solutions for the set of optimization problems, performs a reduction phase, and provides the optimal solutions within the current region. The reduction phase creates a random sample of points within the current region and identifies a subregion of the current region that very likely does not contain any optimal solutions. The identified subregion is then removed from the current region. If the current region does not satisfies one or more convergence criteria, the process loops back to create another random sample of points and repeats the above-described steps. If, however, the current region does satisfy the convergence criteria, the optimal solutions within the current region are provided to the output device. | 12-23-2010 |
20100325073 | NITROGEN OXIDE SENSITIVE FIELD EFFECT TRANSISTORS FOR EXPLOSIVE DETECTION COMPRISING FUNCTIONALIZED NON-OXIDIZED SILICON NANOWIRES - An apparatus for detecting volatile compounds derived from explosive materials with very high sensitivity. The apparatus is composed of field effect transistors of non-oxidized silicon nanowires modified with specific functional groups including, in particular, amine, imine and/or carboxyl moieties. Further a system is provided comprising the apparatus in conjunction with learning and pattern recognition algorithms and methods of use thereof for detecting and quantifying specific explosive compounds. | 12-23-2010 |
20100325074 | REMOTE MONITORING THRESHOLDS - Apparatus for generating a threshold value indicative of a status change, comprising a trend projection engine for processing a plurality of sensed values in an order of value size to generate a corresponding predicted value for each of the plurality of sensed values, by reference to a value sequence trend of the plurality of sensed values, a comparator for comparing one or more of the plurality of sensed values against their corresponding predicted values to identify an abnormal sensed value which differs from its corresponding predicted value by a pre-specified amount, and a threshold generator for using the abnormal sensed value to identify the threshold value. | 12-23-2010 |
20100332422 | Policy Evolution With Machine Learning - A method for constructing a classifier which maps an input vector to one of a plurality of pre-defined classes, the method steps includes receiving a set of training examples as input, wherein each training example is an exemplary input vector belonging to one of the pre-defined classes, learning a plurality of functions, wherein each function maps the exemplary input vectors to a numerical value, and determining a class for the input vector by combining numerical outputs of the functions determined for the input vector. | 12-30-2010 |
20100332423 | GENERALIZED ACTIVE LEARNING - Active learning is extended to decisions on information acquisition of both missing labels and missing features within one or more cases. In one example, desired (e.g., optimal) information to acquire about a case at hand and about cases in a training library during diagnostic sessions can be computed concurrently. A joint distribution of variables, comprising observed and unobserved labels and features for one or more cases, is modeled and probability distributions are determined for unobserved variables. An unobserved variable is selected from the joint distribution that has a return on information (ROI) metric having a combination of a desired uncertainty metric for a value of the unobserved variable and a desired cost for observing the value of the unobserved variable. The value of the variable is observed, and the probability distributions for the respective unobserved variables in the joint distribution are updated using the value of the identified variable. | 12-30-2010 |
20100332424 | DETECTING FACTUAL INCONSISTENCIES BETWEEN A DOCUMENT AND A FACT-BASE - Techniques for identifying one or more inconsistencies between an unstructured document and a back-end fact-base are provided. The techniques include automatically parsing a query document and comparing the document with a back-end fact-base comprising facts relevant to the document, identifying one or more inconsistencies between information mentioned in the document and the facts stored in the back-end fact-base, and providing a response to the query document, wherein the response additionally includes the one or more identified inconsistencies. | 12-30-2010 |
20100332425 | Method for Clustering Samples with Weakly Supervised Kernel Mean Shift Matrices - A method clusters samples using a mean shift procedure. A kernel matrix is determined from the samples in a first dimension. A constraint matrix and a scaling matrix are determined from a constraint set. The kernel matrix is projected to a feature space having a second dimension using the constraint matrix, wherein the second dimension is higher than the first dimension. Then, the samples are clustered according to the kernel matrix. | 12-30-2010 |
20100332426 | METHOD OF IDENTIFYING LIKE-MINDED USERS ACCESSING THE INTERNET - A method of identifying like-minded users accessing the Internet, comprises presenting a multimedia item to a user on an Internet site visited by the user. The user is offered at least one mechanism to provide a response to the item. Responses to the item from a plurality of users are collected. Those people providing the same response to the item are identified as like-minded. | 12-30-2010 |
20100332427 | Data processing apparatus generating motion of 3D model and method - Provided is a data processing apparatus that may include a storage unit, a first calculator, and a second calculator. The storage unit may store a plurality of training data obtained by motion sensing. The first calculator may calculate a first transformation matrix by performing a regression analysis for the plurality of training data. The second calculator may calculate first output data by applying the first transformation matrix to first input data. | 12-30-2010 |
20100332428 | ELECTRONIC DOCUMENT CLASSIFICATION - An electronic document classification system disclosed herein classifies electronic documents. The classification of the documents may involve analyzing the document and the information attached to the document to generate a set of classification data and comparing the classification data with one or more classification rules to generate a set of classifying data. The system attaches the set of classifying data to the electronic document and displays the electronic document based on the set of classifying data. The classification data may also be used to prioritize the electronic documents and to assign a retention period to the electronic documents. The system is further adapted to receive user feedback regarding the classification of the electronic document and to update the classification rules. | 12-30-2010 |
20110004573 | IDENTIFYING TRAINING DOCUMENTS FOR A CONTENT CLASSIFIER - Systems, methods and articles of manufacture are disclosed for identifying a training document for a content classifier. One or more thresholds may be defined for designating a document as a training document for a content classifier. A plurality of documents may be evaluated to compute a score for each respective document. The score may represent suitability of a document for training the content classifier with respect to a category. The score may be computed based on content of the plurality of documents, metadata of the plurality of documents, link structure of the plurality of documents, user feedback (e.g., user supplied document tags) received for the plurality of documents, and document metrics received for the plurality of documents. Based on the computed scores, a training document may be selected. The content classifier may be trained using the selected training document. | 01-06-2011 |
20110004574 | EXECUTION ALLOCATION COST ASSESSMENT FOR COMPUTING SYSTEMS AND ENVIRONMENTS INCLUDING ELASTIC COMPUTING SYSTEMS AND ENVIRONMENTS - Techniques for allocating individually executable portions of executable code for execution in an Elastic computing environment are disclosed. In an Elastic computing environment, scalable and dynamic external computing resources can be used in order to effectively extend the computing capabilities beyond that which can be provided by internal computing resources of a computing system or environment. Machine learning can be used to automatically determine whether to allocate each individual portion of executable code (e.g., a Weblet) for execution to either internal computing resources of a computing system (e.g., a computing device) or external resources of an dynamically scalable computing resource (e.g., a Cloud). By way of example, status and preference data can be used to train a supervised learning mechanism to allow a computing device to automatically allocate executable code to internal and external computing resources of an Elastic computing environment. | 01-06-2011 |
20110004575 | System and Method for Controlling Power Consumption in a Computer System Based on User Satisfaction - Systems and methods for controlling power consumption in a computer system are disclosed. The computer system may be trained, for example, to determine relationship information between user satisfaction and discrete frequencies at which a processor of the computer system runs. The determined relationship can distinguish between different users and different interactive applications. A frequency may be selected from the discrete frequencies at which the processor of the computer system runs based on the determined relationship information for a particular user and a particular interactive application running on the processor of the computer system. The processor may be adapted to run at the selected frequency. | 01-06-2011 |
20110004576 | Systems & methods for improving recognition results via user-augmentation of a database - A system improves recognition results. The system receives multimedia data and recognizes the multimedia data based on training data to generate documents. The system receives user augmentation relating to one of the documents or new documents from a user. The system supplements the training data with the user augmentation or new documents and retrains based on the supplemented training data. | 01-06-2011 |
20110004577 | EMOTION MODEL, APPARATUS, AND METHOD FOR ADAPTIVELY MODIFYING PERSONALITY FEATURES OF EMOTION MODEL - Disclosed are an emotion model, and an apparatus and method for adaptively learning personality of the emotion model. The emotion model, which maintains personality information, creates emotion information according to the personality information and takes a predetermined behavior according to the emotion information. The personality information may change adaptively in correspondence to a user's response to the behavior performed by the emotion model. Accordingly, the emotion model may react adaptively to the user through interactions with the user. | 01-06-2011 |
20110004578 | ACTIVE METRIC LEARNING DEVICE, ACTIVE METRIC LEARNING METHOD, AND PROGRAM - A metric application unit receives data under analysis having a plurality of attributes and a metric indicative of the distance between the data under analysis, calculates the distance between the data under analysis, and output and stores a data analysis result which is generated from an analysis on the data under analysis with a predetermined function, using the calculated distance between the data under analysis. A metric optimization unit generates side-information based on an indication of feedback information entered from the outside and including either similarities between the data under analysis, or the attributes, or a combination thereof, generates a metric which complies with a predetermined condition, based on the generated side information, and stores the generated metric in a metric learning result storage unit. | 01-06-2011 |
20110010316 | PROVIDING A SEAMLESS CONVERSATION SERVICE BETWEEN INTERACTING ENVIRONMENTS - An approach that provides a seamless conversation service between interacting environments is described. In one embodiment, there is a seamless conversation service tool that includes a conversation commencement component configured to facilitate commencement of a conversation between two or more parties occurring over a communication path in one of two or more interacting environments. A user context monitoring component is configured to monitor a user context associated with the conversation. A user context change identification component is configured to identify a change in the user context of the conversation. A conversation transfer component is configured to transfer the conversation between the two or more interacting environments in response to the identified change in the user context, while maintaining a transparency of functionality of the communication path. | 01-13-2011 |
20110010317 | INFORMATION PROCESSING APPARATUS ENABLING DISCRIMINATOR TO LEARN AND METHOD THEREOF - An information processing apparatus includes a preliminary learning unit configured to learn a preliminary discriminator for a respective one of a plurality of combinations of variations in variation categories in a discrimination target pattern, a branch structure determination unit configured to perform discrimination processing using the preliminary discriminator and to determine a branch structure of a main discriminator based on a result of the discrimination processing, and a main learning unit configured to learn the main discriminator based on the branch structure. | 01-13-2011 |
20110010318 | SYSTEM AND METHOD FOR EMPIRICAL ENSEMBLE- BASED VIRTUAL SENSING - An empirical ensemble based virtual sensor system (VS) for the estimation of an amount of water (C) or oil (A) in a fluid mixture, said virtual sensor comprising two or more empirical models (NN | 01-13-2011 |
20110010319 | CORRESPONDENCE LEARNING APPARATUS AND METHOD AND CORRESPONDENCE LEARNING PROGRAM, ANNOTATION APPARATUS AND METHOD AND ANNOTATION PROGRAM, AND RETRIEVAL APPARATUS AND METHOD AND RETRIEVAL PROGRAM - An image data processing system has a learning storage apparatus that stores projection matrixes obtained by canonical correlation analysis so as to derive, based on at least one of an image feature and a word feature, a latent variable as an abstract concept used for associating an image with a word corresponding thereto and that further stores information required for obtaining the latent variable acquired by use of the projection matrixes, a probability of occurrence of an arbitrary image feature from a certain latent variable and a probability of occurrence of an arbitrary word feature from a certain latent variable. In this way, a probability of the image feature and word feature being simultaneously outputted can be easily and quickly determined, thereby executing a high-speed annotation or retrieval with high precision. | 01-13-2011 |
20110010320 | Method, System, And Computer Program Product For Delivering Smart Services - A method, system, and computer program product are described for delivering smart services. According to an exemplary embodiment, a method for delivering smart services includes receiving a request to determine an availability of a service subscriber for responding to an event associated with a service. The service is defined in terms of the event and a situation of the service subscriber. A current situation of the service subscriber is determined using subscriber context information based on private information of the subscriber. Attributes of the event and the current subscriber situation are used to provide to the service at least one of the subscriber context information and a probability related to an availability of the subscriber for responding to the event, allowing the service to generate a response to the event on behalf of the subscriber without the service having direct access to the private subscriber information. | 01-13-2011 |
20110010321 | MARKOVIAN-SEQUENCE GENERATOR AND NEW METHODS OF GENERATING MARKOVIAN SEQUENCES - A new type of Markovian sequence generator and generation method generates a Markovian sequence having controllable properties, notably properties that satisfy at least one control criterion which is a computable requirement holding on items in the sequence. The Markovian sequence is generated chunkwise, each chunk containing a plurality of items in the sequence. During generation of each chunk a search is performed in the space of Markovian sequences to find a chunk-sized series of items which enables the control criterion to be satisfied. The search can be performed using a generate and test approach in which chunk-sized Markovian sequences are generated then tested for compliance with the requirement(s) of the control criteria. Alternatively, the search can be performed by formulating the sequence-generation task as a constraint satisfaction problem, with one or more constraints ensuring that the generated sequence is Markovian and one or more constraints enforcing the requirement(s) of the control criteria. The sequence generator can be used in an interactive system where a user specifies the control criterion via an inputting device ( | 01-13-2011 |
20110010322 | METHOD AND SYSTEM FOR TRANSITIONING FROM A CASE-BASED CLASSIFIER SYSTEM TO A RULE-BASED CLASSIFIER SYSTEM - A computer implemented method including determining whether a predetermined condition is satisfied with respect to a case-based dataset stored within memory accessible by at least one processor; generating, for each of a plurality of rule-based classifiers, rule-based classification data identifying class boundaries between the records upon determining that the predetermined condition has been satisfied; computing a structural risk of the rule-based classification data for each of the rule-based classifiers with respect to the records identified within the record data; selecting rule-based classifiers having generated rule-based classification data identifying class boundaries between the records with a structural risk below a predetermined threshold; identifying selected rule-based classifiers having rule-based classification data that is within a predetermined degree of similarity with the case-based classification data; and replacing the case-based dataset stored within the memory with rule-based classification data of at least one of the identified rule-based classifiers. | 01-13-2011 |
20110016065 | EFFICIENT ALGORITHM FOR PAIRWISE PREFERENCE LEARNING - In one embodiment, training a ranking model comprises: accessing the ranking model and an objective function of the ranking model; accessing one or more preference pairs of objects, wherein for each of the preference pairs of objects comprising a first object and a second object, there is a preference between the first object and the second object with respect to the particular reference, and the first object and the second object each has a feature vector comprising one or more feature values; and training the ranking model by minimizing the objective function using the preference pairs of objects, wherein for each of the preference pairs of objects, a difference between the first feature vector of the first object and the second feature vector of the second object is not calculated. | 01-20-2011 |
20110016066 | AUTOMATIC CONFIGURATION AND CONTROL OF DEVICES USING METADATA - Particular embodiments generally relate to automatically controlling an item. For example, items may include electronic devices, such as televisions, lights, etc, and/or virtual devices, such as applications, etc. In one embodiment, items may be configured using metatags. When a device is connected for operation, one or more metatags for the device are received. A metatag be used to classify the device. For example, the metatag may indicate uses, locations, connections, etc. The use of device (e.g., pathway, reading, etc.) classifies the item in way it can be used. For example, a user may use a pathway light in different ways, such as the user may turn on all lights with the pathway metatag at night. The location indicates the location of the item, such as in the living room, bedroom, etc. The connections indications a type of item, such as a bedroom light, lamp, TV, etc. | 01-20-2011 |
20110016067 | PROBABILISTIC DECISION MAKING SYSTEM AND METHODS OF USE - Embodiments of this invention comprise modeling a subject's state and the influence of training scenarios, or actions, on that state to create a training policy. Both state and effects of actions are modeled as probabilistic using Partially Observable Markov Decision Process (POMDP) techniques. The POMDP is well suited to decision-theoretic planning under uncertainty. Utilizing this model and the resulting training policy with real world subjects creates a surprisingly effective decision aid for instructors to improve learning relative to a traditional scenario selection strategy. POMDP provides a more valid representation of trainee state and training effects, thus it is capable of producing more valid recommendations concerning how to structure training to subjects. | 01-20-2011 |
20110022549 | Presenting Search Results Based on User-Customizable Criteria - In one embodiment, ranking search results generated in response to search queries comprises: receiving, a search query from a user; identifying a plurality of network contents in response to the search query; determining one or more ranking criteria for the search query; presenting the ranking criteria to the user; receiving from the user one or more weights assigned to one or more of the ranking criteria; ranking the identified network contents based on the ranking criteria and the weights; and presenting the network contents to the user in an order according to their ranking. | 01-27-2011 |
20110022550 | MIXING KNOWLEDGE SOURCES WITH AUTO LEARNING FOR IMPROVED ENTITY EXTRACTION - The disclosed embodiments of computer systems and techniques utilize an ensemble semantics framework to combine knowledge acquisition systems that yield significantly higher quality resources than each system in isolation. Gains in entity extraction are achieved by combining state-of-the-art distributional and pattern-based systems with a large set of features from, for example, a webcrawl, query logs, and wisdom of the crowd sources. This results in improved query interpretation and greater relevancy in providing search results and advertising, for example. | 01-27-2011 |
20110022551 | METHODS AND SYSTEMS FOR GENERATING SOFTWARE QUALITY INDEX - Methods, systems and computer program code (software) products for generating a software quality index descriptive of quality of a given body of software code include identifying, by analysis of the body of software code, fault-prone files in the body of software code; constructing and training, by analysis of the body of software code, a model derived from analysis of the body of software code; and generating, based on the model, an index score representative of the quality of the body of software code. | 01-27-2011 |
20110022552 | Systems and Methods for Implementing a Machine-Learning Agent to Retrieve Information in Response to a Message - Mixed-initiative message-augmenting agent systems and methods that provide users with tools that allow them to respond to messages, such as email messages, containing requests for information or otherwise requiring responses that require information that needs to be retrieved from one or more data sources. The systems and methods allow users to train machine-learning agents how to retrieve and present information in responses to like messages so that the machine-learning agents can eventually automatedly generate responses with minimal involvement by the users. Embodiments of the systems and methods allow users to build message-augmenting forms containing the desired information for responding to messages and to demonstrate to the machine-learning agents where to retrieve pertinent information for populating the forms. Embodiments of the systems and methods allow users to modify and repair automatically generated forms to continually improve the knowledge of the machine-learning agents. | 01-27-2011 |
20110022553 | DIAGNOSIS SUPPORT SYSTEM, DIAGNOSIS SUPPORT METHOD THEREFOR, AND INFORMATION PROCESSING APPARATUS - A diagnosis support system includes a learning unit which calculates a first learning result based on diagnosis results on case data which are obtained by a plurality of doctors and a second learning result based on a diagnosis result on the case data which is obtained by a specific doctor, an analysis unit which analyzes a feature associated with diagnosis by the specific doctor based on a comparison between the first learning result and the second learning result, and a decision unit which decides display information of clinical data obtained by examination of a patient based on the analysis result. | 01-27-2011 |
20110029463 | APPLYING NON-LINEAR TRANSFORMATION OF FEATURE VALUES FOR TRAINING A CLASSIFIER - A collection of labeled training cases is received, where each of the labeled training cases has at least one original feature and a label with respect to at least one class. Non-linear transformation of values of the original feature in the training cases is applied to produce transformed feature values that are more linearly related to the class than the original feature values. The non-linear transformation is based on computing probabilities of the training cases that are positive with respect to the at least one class. The transformed feature values are used to train a classifier. | 02-03-2011 |
20110029464 | SUPPLEMENTING A TRAINED MODEL USING INCREMENTAL DATA IN MAKING ITEM RECOMMENDATIONS - Incremental training data is used to supplement a trained model to provide personalized recommendations for a user. The personalized recommendations can be made by taking into account the user's behavior, such as, without limitation, the user's short and long term web page interactions, to identify item recommendations. A trained model is generated from training data indicative of the web page interaction data collected from a plurality of users. Incremental training data indicative of other web page interaction data can be used to supplement the trained model, or in place of the trained model. Incremental training data can be indicative of user behavior collected more recently than the data used to train the model, for example. | 02-03-2011 |
20110029465 | DATA PROCESSING APPARATUS, DATA PROCESSING METHOD, AND PROGRAM - A data processing apparatus includes an obtaining unit configured to obtain time-series data from a wearable sensor, an activity model learning unit configured to learn an activity model representing a user activity state as a stochastic state transition model from the obtained time-series data, a recognition unit configured to recognize a current user activity state by using the activity model of the user obtained by the activity model learning unit, and a prediction unit configured to predict a user activity state after a predetermined time elapses from a current time from the current user activity state recognized by the recognition unit. | 02-03-2011 |
20110029466 | SUPERVISED RANK AGGREGATION BASED ON RANKINGS - A method and system for rank aggregation of entities based on supervised learning is provided. A rank aggregation system provides an order-based aggregation of rankings of entities by learning weights within an optimization framework for combining the rankings of the entities using labeled training data and the ordering of the individual rankings. The rank aggregation system is provided with multiple rankings of entities. The rank aggregation system is also provided with training data that indicates the relative ranking of pairs of entities. The rank aggregation system then learns weights for each of the ranking sources by attempting to optimize the difference between the relative rankings of pairs of entities using the weights and the relative rankings of pairs of entities of the training data. | 02-03-2011 |
20110035344 | COMPUTING MIXED-INTEGER PROGRAM SOLUTIONS USING MULTIPLE STARTING VECTORS - An optimization engine includes a mixed-integer programming (MIP) solver that receives a programming model, an outcome objective, and a group of start vectors. Each of the MIP start vectors in the group specify one or more restrictions to apply to the programming model. The MIP solver uses the programming model to compute a potential solution from each of the MIP start vectors included in the group, which results in a group of potential solutions. Next, the MIP solver selects one of the potential solutions in the group as an optimal intra-group solution. The optimal intra-group solution is the potential solution in the group that best achieves the outcome objective. In turn, the optimal intra-group solution is used to complete the outcome objective. | 02-10-2011 |
20110035345 | AUTOMATIC CLASSIFICATION OF SEGMENTED PORTIONS OF WEB PAGES - Exemplary methods and apparatuses are provided which may be used for classifying and indexing segmented portions of web pages and providing related information for use in information extraction and/or information retrieval systems. | 02-10-2011 |
20110035346 | METHOD AND SYSTEM FOR DATA ANALYSIS AND SYNTHESIS | 02-10-2011 |
20110040706 | SCALABLE TRAFFIC CLASSIFIER AND CLASSIFIER TRAINING SYSTEM - A traffic classifier has a plurality of binary classifiers, each associated with one of a plurality of calibrators. Each calibrator trained to translate an output score of the associated binary classifier into an estimated class probability value using a fitted logistic curve, each estimated class probability value indicating a probability that the packet flow on which the output score is based belongs to the traffic class associated with the binary classifier associated with the calibrator. The classifier training system configured to generate a training data based on network information gained using flow and packet sampling methods. In some embodiments, the classifier training system configured to generate reduced training data sets, one for each traffic class, reducing the training data related to traffic not associated with the traffic class. | 02-17-2011 |
20110040707 | INTELLIGENT MUSIC SELECTION IN VEHICLES - A method of intelligent music selection in a vehicle includes learning user preferences for music selection in the vehicle corresponding to a plurality of driving conditions of the vehicle. Input is received that is indicative of a current driving condition of the vehicle. And, music is selected and played based on the learned user preferences for music selection in the vehicle corresponding to the current driving condition. | 02-17-2011 |
20110040708 | MULTIPLE ENTRY POINT NETWORK FOR STREAM SUPPORT IN A RULE ENGINE - Some embodiments of a multiple entry point network for stream support in an exemplary rule engine have been presented. In one embodiment, a stream of events is asserted into a working memory of a rule engine, which supports event processing. The rule engine, running on a server, processes the stream of events against a set of rules retrieved from a rule repository of the rule engine. To process the events, the rule engine may construct a network having multiple root nodes, each being an entry point into the network, through which the events may enter the network and propagate through the network. | 02-17-2011 |
20110040709 | PATTERN BEHAVIOR SUPPORT IN A RULE ENGINE - Some embodiments of pattern behavior support in a rule engine have been presented. In one embodiment, a behavior builder registry is stored on a computer-readable storage device in a server. The behavior builder registry includes a set of behaviors supported by a rule engine and a set of builders associated with the behaviors. A compiler running on the server may compile a set of rules using the behavior builder registry to generate a network having a set of nodes. In response to a data object asserted propagating into a node of the network, the rule engine may first check one or more behaviors at the node before applying one or more regular constraints at the node on the data object asserted. | 02-17-2011 |
20110040710 | STRUCTURED DIFFERENTIAL LEARNING - A method and information processing system train a control system using structured differential learning. A set of features extracted from a set of input data is analyzed by a plurality of analyzing components. An output response is generated by each analyzing component in the plurality of analyzing components for each feature regarding whether the each feature has an acceptable value associated therewith relative to a value of the parameter associated with the each analyzing component. A confidence score is associated with the each output response. Each output response and each confidence score is combined into a single final output response and single final confidence score. An analyzing component is identified from the plurality of analyzing components that is a strongest candidate for generating an incorrect final output response based at least on the confidence score. | 02-17-2011 |
20110040711 | TRAINING A CLASSIFIER BY DIMENSION-WISE EMBEDDING OF TRAINING DATA - A classifier training method and apparatus for training, a linear classifier trained by the method, and its use, are disclosed. In training the linear classifier, signatures for a set of training samples, such as images, in the form of multi-dimension vectors in a first multi-dimensional space, are converted to a second multi-dimension space, of the same or higher dimensionality than the first multi-dimension space, by applying a set of embedding functions, one for each dimension of the vector space. A linear classifier is trained in the second multi-dimension space. The linear classifier can approximate the accuracy of a non-linear classifier in the original space when predicting labels for new samples, but with lower computation cost in the learning phase. | 02-17-2011 |
20110040712 | Extensions to Semantic Net - A semantic network includes a number of nodes are interconnected to one another through links (e.g., in a subject/verb/target form) representing relationships between the nodes and one or more of the links have one or more variants representing qualifications of the relationships between the nodes. For each link having one or more variants, the variants may be ordered in configurations. Such ordering of the variants in the configurations may be self-described within the semantic network and may determine precedence of those links belonging to the variants. Some of the links of the network may be nodes of others of the links. The interconnection of at least some of the nodes may define a meta-meta model that defines terms in which particular meta models can be defined, each meta model comprising meta facts regarding the nodes of the semantic network. | 02-17-2011 |
20110047105 | Use of Machine Learning for Classification of Magneto Cardiograms - The use of machine learning for pattern recognition in magnetocardiography (MCG) that measures magnetic fields emitted by the electrophysiological activity of the heart is disclosed herein. Direct kernel methods are used to separate abnormal MCG heart patterns from normal ones. For unsupervised learning, Direct Kernel based Self-Organizing Maps are introduced. For supervised learning Direct Kernel Partial Least Squares and (Direct) Kernel Ridge Regression are used. These results are then compared with classical Support Vector Machines and Kernel Partial Least Squares. The hyper-parameters for these methods are tuned on a validation subset of the training data before testing. Also investigated is the most effective pre-processing, using local, vertical, horizontal and two-dimensional (global) Mahanalobis scaling, wavelet transforms, and variable selection by filtering. | 02-24-2011 |
20110055121 | SYSTEM AND METHOD FOR IDENTIFYING AN OBSERVED PHENEMENON - A system for identifying an observed phenomenon. The system includes a computing device configured for receiving disparate data streams associated with disparate data sources. The system also includes a feature extraction module communicably connected to the computing device, a classification module communicably connected to the computing device, and a consensus module communicably connected to the computing device. The feature extraction module is configured for generating a set of attributes for each data stream. The classification module is configured for soft associating labels with attributes for each set of attributes, and for generating a confidence value for each soft association. The consensus module is configured for generating an output indicative of the phenomenon. The consensus module includes a standardization module and a sequential data module. The standardization module is configured for standardizing the confidence values. The sequential data module is configured for generating the output based on the standardized confidence values. | 03-03-2011 |
20110055122 | MONITORING WITH ADAPTIVE DYNAMIC CLASSIFICATION - In a monitoring method, a time sequence of information pertaining to a monitored device, network, or system is recorded, comprising observations of the monitored device, network, or system and known prior correct action recommendations for the monitored device, network, or system. A hidden Markov model (HMM) operating on the time sequence of information is maintained. The HMM comprises a hidden state of the monitored device, network, or system. A current state of the monitored device, network, or system is classified using a classification value comprising an emission of the HMM that depends on an estimate of the distribution of the hidden state and on a selected portion of the time sequence of information. An action recommendation is generated for the current state of the monitored device, network, or system based on the classification value. | 03-03-2011 |
20110055123 | Systems and Methods for Using Multiple In-line Heuristics to Reduce False Positives - An exemplary method for using multiple in-line heuristics to reduce false positives may include: 1) training a first heuristic using a set of training data, 2) deploying the first heuristic, 3) identifying false positives produced by the first heuristic during deployment, 4) modifying the training data to include the false positives produced by the first heuristic, 5) creating a second heuristic using the modified training data, 6) deploying both the first heuristic and the second heuristic, and then 7) applying both the first heuristic and the second heuristic, in sequence, to a set of field data. | 03-03-2011 |
20110055124 | Development of personalized plans based on acquisition of relevant reported aspects - A computationally implemented method includes, but is not limited to: acquiring one or more relevant reported aspects associated with one or more source users that are relevant to achieving one or more target outcomes, the acquisition of the one or more relevant reported aspects being based, at least in part, on relevancy of the one or more relevant reported aspects with respect to the achievement of the one or more target outcomes; and developing one or more personalized plans designed to facilitate an end user to achieve the one or more target outcomes when one or more emulatable aspects indicated by the one or more personalized plans are emulated, the development of the one or more personalized plans being based, at least in part, on the acquiring In addition to the foregoing, other method aspects are described in the claims, drawings, and text forming a part of the present disclosure. | 03-03-2011 |
20110055125 | Template development based on sensor originated reported aspects - A computationally implemented method includes, but is not limited to: providing one or more reported aspects associated with one or more source users that were originally reported by one or more sensors; and developing one or more templates designed to facilitate one or more end users to achieve one or more target outcomes when one or more emulatable aspects indicated by the one or more templates are emulated, the development of the one or more templates being based at least on a portion of the one or more reported aspects In addition to the foregoing, other method aspects are described in the claims, drawings, and text forming a part of the present disclosure. | 03-03-2011 |
20110055126 | Target outcome based provision of one or more templates - A computationally implemented method includes, but is not limited to: receiving one or more requests indicating at least one or more target outcomes of one or more particular templates, the one or more particular templates designed to facilitate one or more end users to achieve the one or more target outcomes when one or more emulatable aspects included in the one or more particular templates are emulated; and providing from a plurality of templates the one or more particular templates, the providing being based at least on the one or more particular templates' association with the one or more target outcomes, the one or more particular templates developed based on one or more reported aspects of one or more source users In addition to the foregoing, other method aspects are described in the claims, drawings, and text forming a part of the present disclosure. | 03-03-2011 |
20110055127 | MODEL OPTIMIZATION SYSTEM USING VARIABLE SCORING - A model optimization system is configured to determine quality of variables for model generation. A data storage stores input variables, quality metrics for the input variables, and weights for the quality metrics. The quality metrics describe sufficiency of data for the input variables and the data is provided for a plurality of regions. A scoring module determines a score for each region based on the input variables and the weighted quality metrics. An optimizer determines whether at least one of the input variables for a region is to be modified based on the scores, and determines whether the total score for the region is operable to be improved using a modified input variable. | 03-03-2011 |
20110060703 | METHOD AND SYSTEM FOR DETECTING CORRELATION IN DATA SETS - A method and system for detecting correlations in a data set is provided. The method includes determining one or more parameters associated with one or more data sets. The one or more parameters are determined at runtime for generating one or more test data sets from the one or more data sets. A test data of the one or more test data sets comprises one or more objects and one or more indices. The one or more objects are associated with the one or more indices. The method further includes computing one or more correlation coefficients associated with the one or more test data sets. The one or more correlation coefficients are computed for detecting correlation corresponding to the one or more test data sets. | 03-10-2011 |
20110060704 | DEPENDENCY GRAPH IN DATA-DRIVEN MODEL - The inference of a dependency graph that represents a graph of solves that leads from input model parameter(s) to output model parameters using analytics. In one embodiment, the dependency graph is part of visually driven analytics in which the output model parameter(s) are used to formulate data-drive scenes. As the identity of the input and/or output model parameter(s) change, or as the analytics themselves change, the dependency graph may also change. This might trigger a resolve of the analytics. In one embodiment, the intermediate parameters involved in the dependency graph may be viewed and evaluated by the user. | 03-10-2011 |
20110060705 | ALERT GENERATION SYSTEM AND METHOD | 03-10-2011 |
20110060706 | INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING METHOD, AND PROGRAM - An information processing device comprising: a likelihood calculating unit configured to take the time series of an observed value to be successively supplied as learned data to be used for learning, and with regard to each module making up a learning model having an HMM (Hidden Markov Model) as a module which is the minimum component, to obtain likelihood that the learned data may be observed at the module; an object module determining unit configured to determine, based on the likelihood, a single module of the learning model, or a new module to be an object module that is an object module having an HMM parameter to be updated; and an updating unit configured to perform learning for updating the HMM parameter of the object module using the learned data. | 03-10-2011 |
20110060707 | INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING METHOD, AND PROGRAM - An information processing device includes: a hierarchy processing unit for generating a unit to connect the unit in a hierarchical structure, the unit including an input control unit for performing input control for storing an observed value, and outputting the time series of the observed value as input data to be given to a learning model having an HMM (Hidden Markov Model) as a minimum component module, a model processing unit for performing processing using the learning model, including a module learning unit for obtaining likelihood of the input data being observed with the module, to determine one module of the learning model, or new module to be an object module having HMM parameters to be updated, and to perform module learning processing for updating the HMM parameters, and a recognizing unit for recognizing the input data using the learning model, and an output control unit for performing output control. | 03-10-2011 |
20110060708 | INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING METHOD, AND PROGRAM - An information processing device includes: an object module determining unit for determining of a learning model having a time series pattern storage model for storing a time series pattern as a module which is the minimum component, a maximum likelihood module having the maximum likelihood, or a new module to be an object module that is a module having a model parameter of the storage model to be updated; and an updating unit for updating the model parameter of the object module using learned data to be used for learning that is the time series of an observed value; with the object module determining unit using the learned data to determine the maximum likelihood module or the new module to be the object module based on the posterior probability of the learning model in the case that learning of the maximum likelihood module or the new module has been performed. | 03-10-2011 |
20110060709 | DATA PROCESSING APPARATUS, DATA PROCESSING METHOD, AND PROGRAM - A data processing apparatus includes an action learning unit configured to train a user activity model representing activity states of a user in the form of a probabilistic state transition model using time-series location data items of the user, an action recognizing unit configured to recognize a current location of the user using the user activity model obtained through the action learning unit, an action estimating unit configured to estimate a possible route for the user from the current location recognized by the action recognizing unit and a selection probability of the route, and a travel time estimating unit configured to estimate an arrival probability of the user arriving at a destination and a travel time to the destination using the estimated route and the estimated selection probability. | 03-10-2011 |
20110066577 | Machine Learning Using Relational Databases - Machine learning using relational databases is described. In an embodiment a model of a probabilistic relational database is formed by augmenting relation schemas of a relational database with probabilistic attributes. In an example, the model comprises constraints introduced by linking the probabilistic attributes using factor statements. For example, a compiler translates the model into a factor graph data structure which may be passed to an inference engine to carry out machine learning. For example, this enables machine learning to be integrated with the data and it is not necessary to pre-process or reformat large scale data sets for a particular problem domain. In an embodiment a machine learning system for estimating skills of players in an online gaming environment is provided. In another example, a machine learning system for data mining of medical data is provided. In some examples, missing attribute values are filled using machine learning results. | 03-17-2011 |
20110066578 | METHOD AND SYSTEM FOR PARALLEL STATISTICAL INFERENCE ON HIGHLY PARALLEL PLATFORMS - Methods for faster statistical inference in computation based recognition problems on highly parallel processors with multiple cores on-a-chip are disclosed, which include: selectively flattening levels of the recognition network to improve inference speed (improving the recognition model); selectively duplicating parts of the recognition network to minimize a critical section in atomic accesses to as few as one atomic instruction (improving the recognition procedure); and combining weight and source port into one 32-bit word to minimize the number of atomic operations. These methods have been implemented on an NVIDIA GTX 280 processor in a Large Vocabulary Continuous Speech Recognition (LVCSR) embodiment, and achieve more than a 10× speed up compared to a highly optimized sequential implementation on an Intel Core i7 processor. | 03-17-2011 |
20110071964 | BUILDING AND USING PREDICTIVE MODELS OF CURRENT AND FUTURE SURPRISES - Methods are described for identifying events that would be considered surprising by people and identifying how and when to transmit information to a user about situations that they would likely find surprising. Additionally, the methods of identifying surprising situations can be used to build a case library of surprising events, joined with a set of observations before the surprising events occurred. Statistical machine learning methods can be applied with data from the case library to build models that can predict when a user will likely be surprised at future times. One or more models of context-sensitive expectations of people, a view of the current world, and methods for recording streams or events before surprises occur, and for building predictive models from a case library of surprises and such historical observations can be employed. The models of current and future surprises can be coupled with display and alerting machinery. | 03-24-2011 |
20110071965 | SYSTEM AND METHOD FOR CROSS DOMAIN LEARNING FOR DATA AUGMENTATION - According to an example embodiment, a method comprises executing instructions by a special purpose computing apparatus to, for labeled source domain data having a plurality of original labels, generate a plurality of first predicted labels for the labeled source domain data using a target function, the target function determined by using a plurality of labels from labeled target domain data. The method further comprises executing instructions by the special purpose computing apparatus to apply a label relation function to the first predicted labels for the source domain data and the original labels for the source domain data to determine a plurality of weighting factors for the labeled source domain data. The method further comprises executing instructions by the special purpose computing apparatus to generate a new target function using the labeled target domain data, the labeled source domain data, and the weighting factors for the labeled source domain data, and evaluate a performance of the new target function to determine if there is a convergence. | 03-24-2011 |
20110071966 | CONDITION MONITORING OF AN UNDERWATER FACILITY - A method for monitoring the condition of apparatus located at an underwater facility that includes sensing at least one parameter associated with the apparatus, providing a model of expected behaviour of said at least one parameter, comparing said sensed parameter with said model, and assessing the condition of the apparatus based upon said comparison. | 03-24-2011 |
20110071967 | Automatic Labeler Assignment - A method, including receiving multi-labeler data that includes data points labeled by a plurality of labelers; building a model from the multi-labeler data, wherein the model includes an input variable that corresponds to the data points, a label variable that corresponds to true labels for the data points, and variables for the labels given by the labelers; and executing the model, in response to receiving new data points, to determine a level of expertise of the labelers for the new data points. | 03-24-2011 |
20110078097 | SHARED FACE TRAINING DATA - Face data sharing techniques are described. In an implementation, face data for a training image that includes a tag is discovered in memory on a computing system. The face data is for a training image that includes a tag associated with a face. The face data is replicated in a location in memory, on another computing system, so the face data is discoverable. | 03-31-2011 |
20110078098 | METHOD AND SYSTEM FOR EXTRACTION - A system and method for extracting information from at least one document in at least one set of documents, the method comprising: generating, using at least one ranking and/or matching processor, at least one ranked possible match list comprising at least one possible match for at least one target entry on the at least one document, the at least one ranked possible match list based on at least one attribute score and at least one localization score. | 03-31-2011 |
20110078099 | METHOD FOR FEATURE SELECTION AND FOR EVALUATING FEATURES IDENTIFIED AS SIGNIFICANT FOR CLASSIFYING DATA - A group of features that has been identified as “significant” in being able to separate data into classes is evaluated using a support vector machine which separates the dataset into classes one feature at a time. After separation, an extremal margin value is assigned to each feature based on the distance between the lowest feature value in the first class and the highest feature value in the second class. Separately, extremal margin values are calculated for a normal distribution within a large number of randomly drawn example sets for the two classes to determine the number of examples within the normal distribution that would have a specified extremal margin value. Using p-values calculated for the normal distribution, a desired p-value is selected. The specified extremal margin value corresponding to the selected p-value is compared to the calculated extremal margin values for the group of features. The features in the group that have a calculated extremal margin value less than the specified margin value are labeled as falsely significant. | 03-31-2011 |
20110082819 | Systems and Methods for Decision Pattern Identification and Application - A system for decision pattern identification and application in a software engineering project includes a decision pattern miner configured to locate a plurality of decisions in a search space; a decision pattern generator configured to generate a decision pattern from the located decisions; a decision pattern repository configured to store the decision pattern; a decision pattern proposal maker configured to search the decision pattern repository for a decision pattern relevant to a decision space; and a decision pattern propagator configured to propagate the decision pattern relevant to the decision space in the decision space. | 04-07-2011 |
20110082820 | ASSESSING AN ENVIRONMENTAL FOOTPRINT OF AN OBJECT - In a method for assessing an environmental footprint of an object, economic data and environmental data of the object is aggregated and complexity of the object is determined. In addition, the economic data, the environmental data, and the complexity of the object are correlated and a model of an environmental footprint of the object is created based upon the correlation. Moreover, the environmental footprint of the object is assessed through application of the model. | 04-07-2011 |
20110087625 | Systems and Methods for Automatic Creation of Agent-Based Systems - An agent-based system may be automatically generated from a specification provided by a user or third-party process. An agent generator may map the specification to a canonical model identifying one or more tasks to be performed by the agent-based system as ontological concepts. The agent generator may generate one or more candidate agents using the canonical model. The candidate agents may comprise one or more interconnected data transforms, which may comprise data access transforms, preprocessing transforms, machine learning transforms, and/or structural transforms. The agent generator iteratively modifies the agent-based system until a termination criteria is satisfied. The termination criteria may provide a selection mechanism whereby a performance of the plurality of candidate agents may be evaluated. An optimal agent may be selected using, inter alia, the performance of the agent-based system. | 04-14-2011 |
20110087626 | PRODUCT CLASSIFICATION IN PROCUREMENT SYSTEMS - Various embodiments provide solutions to assist in the classification of products in a procurement system. The tools provided by various embodiments include, without limitation, methods, systems, and/or software products. Merely by way of example, a method might comprise one or more procedures, any or all of which are executed by a computer system. Correspondingly, an embodiment might provide a computer system configured with instructions to perform one or more procedures in accordance with methods provided by various other embodiments. Similarly, a computer program might comprise a set of instructions that are executable by a computer system (and/or a processor therein) to perform such operations. In many cases, such software programs are encoded on physical and/or tangible computer readable media (such as, to name but a few examples, optical media, magnetic media, and/or the like). | 04-14-2011 |
20110093414 | SYSTEM AND METHOD FOR PHRASE IDENTIFICATION - A phrase identification system and method are provided. The method comprises: identifying one or more phrase candidates in the electronic document; selecting one of the phrase candidates; numerically representing features of the selected phrase candidates to obtain a numeric feature representation associated with that phrase candidate; and inputting the numeric feature representation into a machine learning classifier, the machine learning classifier being configured to determine, based on each numeric feature representation, whether the phrase candidate associated with that numeric feature representation is a phrase. | 04-21-2011 |
20110093415 | CONTENT RECOMMENDATION APPARATUS AND METHOD - A content recommendation apparatus and method are provided. The content recommendation apparatus may record user history information of a user of a personal communication terminal where a web browsing service or mobile communication is possible. The user history information may be used to generate preference information of the user. Based on the preference information, content may be recommended to the user through a display based on a category type of the content such that different types of content are visually differentiated on the display. | 04-21-2011 |
20110093416 | Systems, Methods, and Media for Performing Classification - Systems, methods, and media that: implement a boosted classifier having a plurality of weak hypotheses that produce a classification, each of the plurality of weak hypotheses having at least one weight; receive testing data; receive at least one piece of training data subsequently to receiving the testing data; calculate corrective terms for correcting a sum of weights of correctly classified training data and a sum of weights of incorrectly classified training data; calculate the sum of weights of correctly classified training data and the sum of weights of incorrectly classified training data based on the corrective terms; modify the at least one weight of at least one of the plurality of weak hypotheses in response to the at least one piece of training data based on the sum of weights to produce modified weights; and classify the testing data based on the modified weights to produce a classification. | 04-21-2011 |
20110093417 | TOPICAL SENTIMENTS IN ELECTRONICALLY STORED COMMUNICATIONS - The present application presents methods for performing topical sentiment analysis on electronically stored communications employing fusion of polarity and topicality. The present application also provides methods for utilizing shallow NLP techniques to determine the polarity of an expression. The present application also provides a method for tuning a domain-specific polarity lexicon for use in the polarity determination. The present application also provides methods for computing a numeric metric of the aggregate opinion about some topic expressed in a set of expressions. | 04-21-2011 |
20110093418 | AI Time Machine - A method for an AI time machine to accept sequential input tasks from at least one user, manage tasks, and execute tasks simultaneously or sequentially. Tasks specified by a user can be accomplished in the virtual world or in the real world and includes extracting digital data from electronic devices or manipulation of objects in the real world. The AI time machine's data structures, comprising: at least one dynamic robot to train the AI time machine; a main program with two modes: training mode and standard mode; external technologies, comprising: universal artificial intelligence programs, human level robots, psychic robots, super intelligent robots, the AI time machine, dynamic robots, a signalless technology, atom manipulators, ghost machines, a universal CPU, an autonomous prediction internet, and a 4-d computer; a videogame environment for virtual characters to do and store work; a prediction internet; a universal brain to store dynamic robot pathways or virtual character pathways, said universal brain, comprising: a real world brain, a virtual world brain, and a time machine world brain; a timeline of Earth that records predicted knowledge of Earth's past, current and future; a future United States government system; and a long-term memory. The present invention further serves as a universal AI to control at least one of the following: a machine, a hierarchical team of machines, a universal machine and a transforming machine. | 04-21-2011 |
20110099130 | Integrated learning for interactive synthetic characters - A practical approach to real-time learning for synthetic characters grounded in the techniques of reinforcement learning and informed by insights from animal training. The approach simplifies the learning task for characters by (a) enabling them to take advantage of predictable regularities in their world, (b) allowing them to make maximal use of any supervisory signals, and (c) making them easy to train by humans. An autonomous animated dog is described that can be trained with a technique used to train real dogs called “clicker training.” | 04-28-2011 |
20110099131 | PAIRWISE RANKING-BASED CLASSIFIER - The present invention provides methods and systems for binary classification of items. Methods and systems are provided for constructing a machine learning-based and pairwise ranking method-based classification model for binary classification of items as positive or negative with regard to a single class, based on training using a training set of examples including positive examples and unlabelled examples. The model includes only one hyperparameter and only one threshold parameter, which are selected to optimize the model with regard to constraining positive items to be classified as positive while minimizing a number of unlabelled items classified as positive. | 04-28-2011 |
20110099132 | PAY ZONE PREDICTION - Implementations of pay zone prediction are described. More particularly, apparatus and techniques described herein allow a user to predict pay zones in wells. By accurately predicting pay zones, the user can perforate an existing well at predefined well depths to access hydrocarbon bearing strata while avoiding other undesirable strata (such as water bearing strata). For example, in one possible implementation, well data and syntactic data from a first set of one or more existing wells can be used to create one or more syntactic models. These syntactic models can then be used with water cut and well data from the one or more existing wells to create a pay zone prediction model which can be used with wells outside of the first set of existing wells. | 04-28-2011 |
20110099133 | Systems and methods for capturing and managing collective social intelligence information - A method for capturing and managing training data collected online includes: receiving a first dataset from one or more online sources; sampling the first dataset and generating a second dataset, the second dataset including the data sampled from the first dataset; receiving an annotated second dataset with predefined labels; and dividing the annotated second dataset into a training dataset and a test dataset. The disclosed method further includes: configuring a machine learning based classifier based on the training dataset; predicting at least one data point based on the training dataset and calculating a confidence score; comparing the at least one predicted data point to the test dataset; sorting the at least one predicted data point based on its confidence score; and receiving corrected training data associated with the at least one predicted data point. | 04-28-2011 |
20110099134 | Method and System for Agent Based Summarization - A method and system for using a proxy agent based access to documents and the corresponding summaries and its subsequent usage is disclosed. The method and system provides for retrieving a document, generating or retrieving summary, generating statistical parameters to judge the summary quality, using text segmentation to judge the quality of the summary, getting user rating input and using it to train a classifier, using the classifier to predict the rating of a summary, displaying the summary along with its rating, and optionally overlaying the summary display with relevant advertising and thus prevent denial of information/information overload and stimulating accelerated learning. | 04-28-2011 |
20110099135 | SYSTEM, METHOD AND COMPUTER PROGRAM PRODUCT FOR EVALUATING A STORAGE POLICY BASED ON SIMULATION - A computer implemented method for generating a storage policy for a storage system based on simulation results associated with a state of the storage system is provided. The method comprises receiving a target function applicable to a storage system, wherein the target function represents a measure of values associated with storage parameters related to productivity and loss tolerance of the storage system; wherein the simulation results for a state of the storage system are calculated based on a least one of (a) the storage system simulated response to a set of simulated file-related storage operation requests generated based on one or more simulation rules, (b) the state of the storage system before responding to the set of simulated file-related storage operation requests, (c) the storage system target function; and (d) rules for simulating file-related storage operation requests. | 04-28-2011 |
20110106732 | METHOD FOR CATEGORIZING LINKED DOCUMENTS BY CO-TRAINED LABEL EXPANSION - Systems and methods are described that facilitate categorizing a group of linked web pages. A plurality of web pages each contains at least one link to another page within the group. A feature analyzer evaluates features associated with the one or more web pages to identify content, layout, links and/or metadata associated with the one or more web pages and identifies features that are labeled and features that are unlabeled. A graphing component creates a vector associated with each web page feature wherein vectors for unlabeled features are determined by their graphical proximity to features that are labeled. A co-training component receives the graph of vectors from the graphing component and leverages the disparate web page features to categorize each aspect of each feature of the page. A page categorizer receives aspect categorization information from the co-training component and categorizes the web page based at least upon this information. | 05-05-2011 |
20110106733 | SYSTEM AND METHOD FOR PATIENT-MANAGED ORAL ANTICOAGULANT THERAPY - A system for facilitating patient-managed anticoagulant therapy having patient portals for receiving patient information associated with a current case. Each patient portal has a patient portal processor, patient portal memory, and an output device, and the patient information including current factors affecting patient reaction to anticoagulant therapy. The system also has a case repository connected to each patient portal, the case repository having a case repository processor and a case repository memory storing previous cases, each case having previous factors affecting patient reaction to anticoagulant therapy and at least one solution. The patient portal processor and/or the case repository processor are programmed for selecting at least one relevant case similar to the current case and provide the solution to that case for application to the current case. A similarity metric is applied to each previous case to determine the similarity of each previous case to the current case. | 05-05-2011 |
20110106734 | SYSTEM AND APPARTUS FOR FAILURE PREDICTION AND FUSION IN CLASSIFICATION AND RECOGNITION - The present invention relates to pattern recognition and classification, more particularly, to a system and method for meta-recognition which can to predict success/failure for a variety of different recognition and classification applications. In the present invention, we define a new approach based on statistical extreme value theory and show its theoretical basis for predicting success/failure based on recognition or similarity scores. By fitting the tails of similarity or distance scores to an extreme value distribution, we are able to build a predictor that significantly outperforms random chance. The proposed system is effective for a variety of different recognition applications, including, but not limited to, face recognition, fingerprint recognition, object categorization and recognition, and content-based image retrieval system. One embodiment includes adapting machine learning approach to address meta-recognition based fusion at multiple levels, and provide an empirical justification for the advantages of these fusion element. This invention provides a new score normalization that is suitable for multi-algorithm fusion for recognition and classification enhancement. | 05-05-2011 |
20110106735 | RECURSIVE FEATURE ELIMINATION METHOD USING SUPPORT VECTOR MACHINES - Identification of a determinative subset of features from within a group of features is performed by training a support vector machine using training samples with class labels to determine a value of each feature, where features are removed based on their the value. One or more features having the smallest values are removed and an updated kernel matrix is generated using the remaining features. The process is repeated until a predetermined number of features remain which are capable of accurately separating the data into different classes. In some embodiments, features are eliminated by a ranking criterion based on a Lagrange multiplier corresponding to each training sample. | 05-05-2011 |
20110106736 | SYSTEM AND METHOD FOR INTUITIVE USER INTERACTION - The disclosed method and apparatus provide prediction and suggestion of proposed actions a user of an electronic device is likely to want to do, at certain circumstances. The actions take into account historical activities made by the user, as well as incoming events, environmental data, external data, or any other source of information. Proposing the actions may be done by one or more engines, each relating to one or more aspects of the device, actions, events, activities, preferences and the like. The actions proposed by all engines are merged and prioritized, and presented to a user. The options are presented to a user in a manner that enables activation of any of the options, with the relevant settings and parameters. | 05-05-2011 |
20110112993 | Search methods and various applications - The present invention relates to a system and method for information process using artificially constructed apparatus. More specially, in one preferred embodiment of the present invention, documents can be processed so that the most relevant terms of the contents of the documents can be obtained, and searched. In another preferred embodiment of the present invention, the present invention provides a system and method that can search for information in a document structure and provide precise results by analyzing the inputs and search results using the executing system and the knowledge structure of the think system. | 05-12-2011 |
20110112994 | MUSICAL PIECE RECOMMENDATION SYSTEM, MUSICAL PIECE RECOMMENDATION METHOD, AND MUSICAL PIECE RECOMMENDATION COMPUTER PROGRAM - A musical piece recommendation system is provided that allows instantaneous registration of a new user and a new musical piece without retraining in a basic training section. A first incremental training section | 05-12-2011 |
20110112995 | Systems and methods for organizing collective social intelligence information using an organic object data model - A method for capturing and organizing intelligence data using an organic data model includes: receiving one or more webpages containing social intelligence data; segmenting content of the one or more webpages containing social intelligence data; identifying named entities in the segmented content of the one or more webpages; identifying topics in the segmented content of the one or more webpages; identifying opinions in the segmented content of the one or more webpages; integrating the identified named entities, topics, and opinions to construct an organic object data model; and storing organic object data associated with the constructed organic object data model in an organic object database. | 05-12-2011 |
20110112996 | Systems and methods for motion recognition using multiple sensing streams - Techniques for motion recognition using multiple data streams are disclosed. Multiple data streams from inertia sensors as well as non-inertial sensors are received to derive a motion recognition signal from motion recognizers. These motion recognizers are originally constructed from a training set of motion signals and may be updated with received multiple sensing signals. In one aspect, multiple data streams are converted to device-independent motion signals that are applied with the motion recognizers to provide a generalized motion recognition capability. | 05-12-2011 |
20110112997 | INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING METHOD, AND PROGRAM - An information processing device includes a model learning unit that carries out learning for self-organization of internal states of a state transition prediction model which is a learning model having internal states, a transition model of the internal states, and an observation model where observed values are generated from the internal states, by using first time series data, wherein the model learning unit learns the observation model of the state transition prediction model after the learning using the first time series data, by fixing the transition model and using second time series data different from the first time series data, thereby obtaining the state transition prediction model having a first observation model where each sample value of the first time series data is observed and a second observation model where each sample value of the second time series data is observed. | 05-12-2011 |
20110119208 | METHOD AND SYSTEM FOR DEVELOPING A CLASSIFICATION TOOL - An exemplary embodiment of the present invention provides a computer implemented method of developing a classifier. The method includes receiving input for a case, the case comprising a plurality of instances and an example, the example comprising a plurality of data fields each corresponding to one of the plurality of instances, wherein the input indicates which, if any, of the instances includes a data field belonging to a target class. The method also includes training the classifier based, at least in part, on the input from the trainer. | 05-19-2011 |
20110119209 | METHOD AND SYSTEM FOR DEVELOPING A CLASSIFICATION TOOL - An exemplary embodiment of the present invention provides a computer implemented method of developing a classifier. The method includes obtaining a set of training data comprising labeled cases. The method also includes training a classifier based, at least in part, on the training data. The method also includes applying the classifier to a plurality of unlabeled cases to generate classification scores for each of the unlabeled cases, wherein each classification score corresponds with an instance of a corresponding case. Furthermore, the classification score corresponding to a first instance in a case is computed based, at least in part, on a value of a case-centric feature corresponding to the first instance, wherein the value of the case-centric feature is based, at least in part, on characteristics of the first instance and a second instance in the case. | 05-19-2011 |
20110119210 | Multiple Category Learning for Training Classifiers - Described is multiple category learning to jointly train a plurality of classifiers in an iterative manner. Each training iteration associates an adaptive label with each training example, in which during the iterations, the adaptive label of any example is able to be changed by the subsequent reclassification. In this manner, any mislabeled training example is corrected by the classifiers during training. The training may use a probabilistic multiple category boosting algorithm that maintains probability data provided by the classifiers, or a winner-take-all multiple category boosting algorithm selects the adaptive label based upon the highest probability classification. The multiple category boosting training system may be coupled to a multiple instance learning mechanism to obtain the training examples. The trained classifiers may be used as weak classifiers that provide a label used to select a deep classifier for further classification, e.g., to provide a multi-view object detector. | 05-19-2011 |
20110119211 | SYSTEM AND METHOD FOR ASSESSING RISK - The invention describes systems and methods of assessing risk using a computer. A computer-based system including an enrollment module, a data aggregation module, a risk assessment module, and a memory is provided for assessing risks. The enrollment receives, at a computer, personal information regarding at least one entity. The data aggregation module receives, at the computer, risk information regarding the entity according to the personal information from at least one data source. The risk assessment module converts the risk information to assessment information. The memory stores the personal information, the risk information, and/or the assessment information on the computer. | 05-19-2011 |
20110119212 | EXPERT SYSTEM FOR DETERMINING PATIENT TREATMENT RESPONSE - A medical digital expert system to predict a patient's response to a variety of treatments (using pre-treatment information) is described. The system utilizes data fusion, advanced signal/information processing and machine learning/inference methodologies and technologies to integrate and explore diverse sets of attributes, parameters and information that are available to select the optimal treatment choice for an individual or for a subset of individuals suffering from any illness or disease including psychiatric, mental or neurological disorders and illnesses. The methodology and system can also be used to determine or confirm medical diagnosis, estimate the level, index, severity or critical medical parameters of the illness or condition, or provide a list of likely diagnoses for an individual suffering/experiencing any illness, disorder or condition. | 05-19-2011 |
20110125678 | GENERATING AN ACTIVITY INFERENCE MODEL FROM CONTEXTUAL DATA - One embodiment provides a system for generating an inference model that determines an activity type for a user from contextual information. During operation, the system receives a set of contextual information associated with the user, wherein the contextual information includes at least a set of location coordinates. The system then determines an association between the contextual information and an activity type. Next, the system generates an activity inference model based in part on the association, wherein the activity inference model takes an instance of contextual information as an input parameter and outputs a corresponding activity type. The model's parameters are based at least on statistics associated with the user's contextual history but not based on the complete contents of the user's contextual history. | 05-26-2011 |
20110125679 | METHOD FOR APPROXIMATING USER TASK REPRESENTATIONS BY DOCUMENT-USAGE CLUSTERING - Embodiments of the present invention provide a system for automatically creating a task representation associated with a user task. The system calculates usage footprints of a document based on other applications, documents, and people that have been accessed by the user within a predetermined time frame before and after the user accesses the document. After obtaining usage footprints of a number of documents, the system applies a clustering technique, such as spectral clustering, to create task representations, each including a collection (cluster) of documents and/or applications that are used for accomplishing a particular task. The system also filters the documents based on their average dwell times, and uses user feedback to merge or split different task clusters in order to provide accurate task representations. | 05-26-2011 |
20110125680 | Effects of Risk Factors on User Health - Risk factor data can be processed by a risk factor coaching engine to determine health risk for a user. The risk factor coaching engine may be executed within a health coaching protocol to perform actions that provide a user with information, recommendations and alerts via other coaching engines, and appointments with health care professionals. The risk factor coaching engine may also predict attribute values for a user based on a time period and goals for user health data upon which the predicted attribute value is based. | 05-26-2011 |
20110125681 | FEATURE EXTRACTION METHOD, FEATURE EXTRACTION APPARATUS, AND FEATURE EXTRACTION PROGRAM - Provided are a feature extraction method of creating a feature vector for objectively evaluating the sequence of aptamer on the basis of the biological features and a feature extraction apparatus and a feature extraction program for performing the method. The feature extraction method according to the present invention includes a step of predicting a secondary structure of a base sequence applied and a step of creating a feature vector based on a predicted secondary structure of the sequence. | 05-26-2011 |
20110125682 | LEARNING DEVICE - Provided is a learning device that represents the learning contents of a learning edition in visual and audio elements such as voice, melody, and image. The learning contents are pointed out by an indicator according to the principle of signal transmission and reception for recognizing the position of the indicator. | 05-26-2011 |
20110131155 | Method for Determining Distributions of Unobserved Classes of a Classifier - A distribution of an unobserved class for a classifier with no known training data is learned by first determining, for each known class, known distribution using known training data. Sufficient statistics of the distribution of the unobserved class are determined from the known distributions and the training data associated with each known class. If the known training data and the known distributions are bounded, then update parameters of the distribution of the unobserved class from the sufficient statistics, else update the parameters from sufficient statistics and a priori probability distributions that specify the distributions of the parameters. | 06-02-2011 |
20110131156 | System, Method and Computer Program Product for Incremental Learning of System Log Formats - A computer program is disclosed including but not limited to instructions to input an initial description of a data format and a batch of data comprising data in a new data format not covered by the initial description, instructions to use the first description to parse the records in the data source, instructions to discard records in the input data that parse successfully, instructions to collect records that fail to parse, instructions to accumulate a quantity, M of records that fail to parse, instructions to return a modified description that extends the initial description to cover the new data, instructions to transform the first description, D into a second description D′ to accommodate differences between the input data format and the first description D by introducing options where a piece of data was missing in the input data and introducing unions where a new type of data was found in the input data; and instructions to use a non-incremental format inference system such as LEARNPADS to infer descriptions for the aggregated portions of input data that did not parse using the first description D. | 06-02-2011 |
20110131157 | SYSTEM AND METHOD FOR PREDICTING CONTEXT-DEPENDENT TERM IMPORTANCE OF SEARCH QUERIES - An improved system and method for identifying context-dependent term importance of queries is provided. A query term importance model is learned using supervised learning of context-dependent term importance for queries and is then applied for advertisement prediction using term importance weights of query terms as query features. For instance, a query term importance model for query rewriting may predict rewritten queries that match a query with term importance weights assigned as query features. Or a query term importance model for advertisement prediction may predict relevant advertisements for a query with term importance weights assigned as query features. In an embodiment, a sponsored advertisement selection engine selects sponsored advertisements scored by a query term importance engine that applies a query term importance model using term importance weights as query features and inverse document frequency weights as advertisement features to assign a relevance score. | 06-02-2011 |
20110131158 | INFORMATION PROCESSING APPARATUS AND INFORMATION PROCESSING METHOD - An information processing apparatus that selects a plurality of feature amounts acquired by applying a filter to learning data and generates a discriminator based on the selected feature amounts includes a time specification unit configured to specify a calculation time required for acquiring a feature amount of a selection candidate by applying the filter to the selected feature amounts or the learning data, a precision specification unit configured to specify a precision of a discriminator generated based on the feature amount of the selection candidate and the selected feature amounts, a selection unit configured to select the feature amount of the selection candidate based on the calculation time and the precision, and a generation unit configured to generate the discriminator based on the selected feature amounts. | 06-02-2011 |
20110131159 | SYSTEMS AND METHODS FOR DETECTING THE PRESENCE OF A BIOLOGICAL STATUS USING CLUSTERING - A method for determining the presence of a biological entity. The method may include entering into a digital computer, at least a plurality of first input values associated with a first genetic element (e.g., mecA), a plurality of second input values associated with a second genetic element (femA), and a plurality of third input values associated with a third genetic element (e.g., orfX) associated with a plurality of samples. Each sample includes a first input value in the plurality of first input values, a second input value in the plurality of second input values, and a third input value in the plurality of third input values. The method also includes determining a threshold value associated with the third genetic element, separating the samples using the threshold value into a first set of samples and a second set of samples, clustering the first set of samples in a feature space defined by the first genetic element and the second genetic element, defining a first boundary space using the first set of samples, and defining a second boundary space using the second set of samples. The first and second boundary spaces differentiate a biological entity from other biological statuses. Other embodiments may also include the use of a genetic element such as SCCmec. | 06-02-2011 |
20110131160 | Method and System for Generating A Linear Machine Learning Model for Predicting Online User Input Actions - A method of targeting receives several granular events and preprocesses the received granular events thereby generating preprocessed data to facilitate construction of a model based on the granular events. The method generates a predictive model by using the preprocessed data. The predictive model is for determining a likelihood of a user action. The method trains the predictive model. A system for targeting includes granular events, a preprocessor for receiving the granular events, a model generator, and a model. The preprocessor has one or more modules for at least one of pruning, aggregation, clustering, and/or filtering. The model generator is for constructing a model based on the granular events, and the model is for determining a likelihood of a user action. The system of some embodiments further includes several users, a selector for selecting a particular set of users from among the several users, a trained model, and a scoring module. | 06-02-2011 |
20110131161 | Methods and Systems for Selecting and Presenting Content on a First System Based on User Preferences Learned on a Second System - A method of selecting and presenting content on a first system based on user preferences learned on a second system is provided. The method includes receiving a user's input for identifying items of the second content system and, in response thereto, presenting a subset of items of the second content system and receiving the user's selection actions thereof. The method includes analyzing the selected items to learn the user's content preferences for the content of the second content system and determining a relationship between the content of the first and second content systems to determine preferences relevant to items of the first content system. The method includes, in response subsequent user input for items of the first content system, selecting and ordering a collection of items of the first content system based on the user's learned content preferences determined to be relevant to the items of the first content system. | 06-02-2011 |
20110137829 | Method for Selecting Neighborhoods of Training Points for Local Learning - A method selects a subset of training points near a query point from a set of training points. The subset of training points near the query point is determined from a the set of training points such that a cumulative similarity is maximized, wherein the cumulative similarity measures a similarity of the query point to each point in the subset and a similarity of points in the subset to each other. | 06-09-2011 |
20110137830 | FRAMEWORK FOR FINDING ONE OR MORE SOLUTIONS TO A PROBLEM - In an embodiment, information for use in identifying a plurality of sub-solvers may be acquired. The plurality of sub-solvers may be used in a first attempt to find at least one solution to a problem that may be defined in the acquired information. At least two of the sub-solvers in the plurality of sub-solvers may be of different sub-solver types. The sub-solvers may be identified based on the acquired information. One or more starting points for the identified sub-solvers may be identified and transferred to the identified sub-solvers. One or more outputs, that indicate one or more results associated with the first attempt to find at least one solution to the problem, may be acquired from the identified sub-solvers. One or more sub-solvers may be identified, based on the acquired one or more outputs, for use in a second attempt to find at least one solution to the problem. | 06-09-2011 |
20110137831 | LEARNING APPARATUS, LEARNING METHOD AND PROGRAM - A learning apparatus includes: an interpolating section which interpolates data missing in time series data; an estimating section which estimates a Hidden Markov Model from the time series data; and a likelihood calculating section which calculates the likelihood of the estimated Hidden Markov Model. The likelihood calculating section calculates the likelihood for normal data which does not have missing data and the likelihood for interpolation data which is interpolated data in different conditions and calculates the likelihood of the Hidden Markov Model for the time series data in which the data is interpolated. The estimating section updates the Hidden Markov Model so that the likelihood calculated by the likelihood calculating section becomes high. | 06-09-2011 |
20110137832 | RELATIONAL BAYESIAN MODELING FOR ELECTRONIC COMMERCE - The present invention provides a language, method and system to formulate and evaluate relational Bayesian networks in an e-commerce environment. The present invention employs a specific language for constructing synthetic variables used to predict events in the Bayesian networks. The present system and language allow for efficient and accurate representation, inference, and discovery of the synthetic variables used to model web visitor behavior. | 06-09-2011 |
20110137833 | DATA PROCESSING APPARATUS, DATA PROCESSING METHOD AND PROGRAM - The data processing apparatus includes a state series generation unit and a computing unit. The state series generation unit generates a time series data of state nodes from a time series data of event. The state transition model of the event is expressed as a stochastic state transition model. The computing unit computes the parameters for the stochastic state transition model of events by computing parameters of time series data corresponding to an appearance frequency of the state nodes, the appearance frequency of transitions among the state nodes and the like. | 06-09-2011 |
20110137834 | LEARNING APPARATUS AND METHOD, PREDICTION APPARATUS AND METHOD, AND PROGRAM - A learning apparatus includes: a location acquiring section for acquiring time series data on locations of a user; a time acquiring section for acquiring time series data on times; and learning section for learning an activity model indicating an activity state of the user as a probabilistic state transition model, using the respective acquired time series data on the locations and the times as an input. | 06-09-2011 |
20110137835 | INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING METHOD, AND PROGRAM - An information processing device includes an acquisition unit acquiring a viewing log including information representing content of an operation for viewing content and time of the operation, a learning unit learning, based on the viewing log acquired by the acquisition unit, a viewing behavior model which is a stochastic state transition model representing a viewing behavior of a user, a recognition unit recognizing, using the viewing behavior model obtained through learning by the learning unit, a current viewing state of the user, a prediction unit predicting, using the viewing behavior model, the viewing behavior of the user after a predetermined period of time with the current viewing state of the user recognized by the recognition unit as a starting point, and a display control unit displaying information relating to content predicted to be viewed through the viewing behavior predicted by the prediction unit. | 06-09-2011 |
20110137836 | METHOD AND SYSTEM FOR GENERATING HISTORY OF BEHAVIOR - Disclosed are method and system for generating history of behavior that is capable of simplifying input of a behavior content of a human behavior pattern determined from data measured by a sensor. A computer obtains biological information measured by a sensor which is mounted to a person and accumulates the biological information, obtains motion frequencies from the accumulated biological information, obtains time-series change points of the motion frequencies, extracts a period between the change points as a scene which is a period of being in the state of an identical motion, compares the motion frequencies with a preset condition for each extracted scene and identifies the action contents in the scene, estimates the behavior content performed by the person in the scene on the basis of the appearance sequence of the action contents, and generates the history of the behaviors on the basis of the estimated behavior contents. | 06-09-2011 |
20110145175 | Methods, Systems and Media Utilizing Ranking Techniques in Machine Learning - Methods, systems and media are taught utilizing ranking techniques in machine learning to learn a ranking function. Specifically, ranking algorithms are applied to learn a ranking function that advantageously minimizes ranking error as a function of targeted ranking order discrepancies between a predetermined first ranking of a training plurality of data elements and a second ranking of the training plurality of data elements by the ranking function. The ranking algorithms taught may be applied to ranking representations of chemical structures and may be particularly advantageous in the field of drug discovery, e.g., for prioritizing chemical structures for drug screenings. | 06-16-2011 |
20110145176 | GENE EXPRESSION PROFILES TO PREDICT BREAST CANCER OUTCOMES - Methods for classifying and for evaluating the prognosis of a subject having breast cancer are provided. The methods include prediction of breast cancer subtype using a supervised algorithm trained to stratify subjects on the basis of breast cancer intrinsic subtype. The prediction model is based on the gene expression profile of the intrinsic genes listed in Table 1. This prediction model can be used to accurately predict the intrinsic subtype of a subject diagnosed with or suspected of having breast cancer. Further provided are compositions and methods for predicting outcome or response to therapy of a subject diagnosed with or suspected of having breast cancer. These methods are useful for guiding or determining treatment options for a subject afflicted with breast cancer. Methods of the invention further include means for evaluating gene expression profiles, including microarrays and quantitative polymerase chain reaction assays, as well as kits comprising reagents for practicing the methods of the invention. | 06-16-2011 |
20110145177 | HIERARCHICAL TEMPORAL MEMORY - Methods and systems for constructing biological-scale hierarchically structured cortical statistical memory systems utilizing fabrication technology and meta-stable switching devices. Learning content-addressable memory and statistical random access memory circuits are detailed. Additionally, local and global signal modulation of bottom-up and top-down processing for the initiation and direction of behavior is disclosed. | 06-16-2011 |
20110145178 | DATA CLASSIFICATION USING MACHINE LEARNING TECHNIQUES - A system and article of manufacture enabling adapting to a shift in document content according to one embodiment of the present invention includes instructions for: receiving at least one labeled seed document; receiving unlabeled documents; receiving at least one predetermined cost factor; training a transductive classifier using the at least one predetermined cost factor, the at least one seed document, and the unlabeled documents; classifying the unlabeled documents having a confidence level above a predefined threshold into a plurality of categories using the classifier; reclassifying at least some of the categorized documents into the categories using the classifier; and outputting identifiers of the categorized documents to at least one of a user, another system, and another process. Systems and articles of manufacture for separating documents are also presented. Systems and articles of manufacture for document searching are also presented. | 06-16-2011 |
20110153528 | PROVIDING COMPARISON EXPERIENCES IN RESPONSE TO SEARCH QUERIES - Computer-readable media, computer systems, and computing devices facilitate providing a comparison experience to a user in response to a search query. Upon receiving a search query from the user, entities are extracted from the query. The entities are associated with entity classes. The entities, entity classes, previous user behavior, and other information are used to infer whether the user likely is engaging in a comparison task. If the inference indicates that the user likely is engaging in a comparison task, a comparison experience is generated and access to the comparison experience is provided to the user. | 06-23-2011 |
20110153529 | METHOD AND APPARATUS TO EFFICIENTLY GENERATE A PROCESSOR ARCHITECTURE MODEL - A method and apparatus for efficiently generating a processor architecture model that accurately predicts performance of the processor for minimizing simulation time are described. In one embodiment, the method comprises: identifying a performance benchmark of a processor; sampling a portion of a design space for the identified performance benchmark; simulating the sampled portion of the design space to generate training data; generating a processor performance model from the training data by modifying the training data to predict an entire design space; and predicting performance of the processor for the entire design space by executing the processor performance model. | 06-23-2011 |
20110153530 | Method and system for analyzing a legacy system based on trails through the legacy system - The present invention concerns a method for analyzing a legacy system ( | 06-23-2011 |
20110153531 | INFORMATION PROCESSING APPARATUS AND CONTROL METHOD FOR THE SAME - An information processing apparatus that encodes input structured data according to an encoding rule is provided. When the structured data matches a specified learning target, this apparatus determines the start of learning of the encoding rule. Upon determining the start of learning, the apparatus recognizes the structure and data type of the structured data and starts learning the encoding rule. The apparatus stores the structured data until an end condition corresponding to the specified learning target holds and the end of learning of the encoding rule is determined. Upon determining the end of learning, the apparatus encodes the stored structured data according to the learned encoding rule. | 06-23-2011 |
20110153532 | DRIVING MANEUVER ASSISTING APPARATUS AND METHOD FOR ASSISTING DRIVING MANEUVER - A driving maneuver assisting apparatus includes a learning section configured to learn a driving-behavior pattern of a driver for a predetermined duration; a non-steady-state degree calculating section configured to calculate a non-steady-state degree by comparing a current driving-behavior pattern with the driving-behavior pattern learned by the learning section, wherein the non-steady-state degree represents how different the current driving-behavior pattern is from the driving-behavior pattern learned by the learning section; a learning level calculating section configured to calculate a learning level of the learning section; and a notifying section configured to notify the driver of a maneuver assisting information for inducing the driving-behavior pattern learned by the learning section in accordance with the learning level calculated by the learning level calculating section, when the non-steady-state degree calculated by the non-steady-state degree calculating section exceeds a threshold value. The notifying section is configured to provide contents of the maneuver assisting information in more detail as the learning level calculated by the learning level calculating section becomes higher. | 06-23-2011 |
20110161258 | Method for Converting Dynamical Systems with Continuous States into Markov Decision Processes with Discrete States - A continuous dynamical system is converted to a Markov decision process (MDP) with discrete states. A predetermined number of continuous states of the continuous system is selected, wherein each continuous state corresponds to one discrete state of the MDP. Delaunay triangulation is applied to the continuous states to produce a set of triangles, wherein vertices of each triangle represent the continuous states. For each discrete state, a next discrete state y=f(x, a) is determined, wherein x represents the continuous state corresponding to the discrete state, a is a control action, and f is a non-linear transition function for the continuous. A particular triangle containing the next discrete state y is identified, and the next discrete state y is expressed as probabilities of transitioning to the discrete states corresponding to the continuous states x represented by the vertices of the particular triangle. | 06-30-2011 |
20110161259 | SYSTEM AND METHOD FOR SIMPLIFICATION OF A MATRIX BASED BOOSTING ALGORITHM - A method for simplification of a matrix based boosting algorithm divides a feature set comprising a plurality of feature data into several subsets, and assigns a number to each subset. The method selects a plurality of number groups including N subsets randomly. The method further computes a value by boosting algorithm according to each of the number groups for obtaining an acceptable false positive value. | 06-30-2011 |
20110161260 | USER-DRIVEN INDEX SELECTION - Techniques for index building are described. Clickcounts of respective training URLs may indicate a number of times that corresponding training URLs were clicked in search engine results. A machine learning algorithm implemented on a computer computes a trained model that is then stored. The clickcounts and respective URLs are passed to the machine learning algorithm to train the model to predict probabilities based on feature vectors of URLs. An index of web pages is built for a set of URLs that identify the web pages. Feature vectors for the URLs are computed. Probabilities of the web pages of the URLs being searched in the future by users may be computed by processing the feature vectors with the trained model. The probabilities may be used to determine which of the URLs to include in the index. | 06-30-2011 |
20110161261 | METHOD AND SYSTEM FOR TRAFFIC PREDICTION BASED ON SPACE-TIME RELATION - A system and method for traffic prediction based on space-time relation are disclosed. The system comprises a section spatial influence determining section for determining, for each of a plurality of sections to be predicted, spatial influences on the section by its neighboring sections; a traffic prediction model establishment section for establishing, for each of the plurality of sections to be predicted, a traffic prediction model by using the determined spatial influences and historical traffic data of the plurality of sections; and a traffic prediction section for predicting traffic of each of the plurality of sections to be predicted for a future time period by using real-time traffic data and the traffic prediction model. An apparatus and method for determining spatial influences among sections, as well as an apparatus and method for traffic prediction, are also disclosed. With the present invention, a spatial influence of a section can be used as a spatial operator and a time sequence model can be incorporated, such that the influences on a current section by its neighboring section for a plurality of spatial orders can be taken into account. In this way, the traffic condition in a spatial scope can be measured more practically, so as to improve accuracy of prediction. | 06-30-2011 |
20110161262 | PROFILE CONFIGURATION FOR A MOBILE COMPUTING DEVICE - Data processing apparatus is disclosed comprising: a sensor module configured to sense a first profile comprised of one or more attributes of an environment of said data processing apparatus; and a classification module configured to assign a prediction factor to each of said one or more attributes of said first profile and to store each said attribute and assigned prediction factor as a stored profile. | 06-30-2011 |
20110167025 | SYSTEMS AND METHODS FOR PARAMETER ADAPTATION - A method of parameter adaptation is used to modify the parameters of a model to improve model performance. The model separately estimates the contribution of each model parameter to the prediction error. It achieves this by transforming to the time-scale plane the vectors of output sensitivities with respect to model parameters and then identifying the regions within the time-scale plane at which the dynamic effect of individual model parameters is dominant on the output. The method then attributes the prediction error in these regions to the deviation of a single parameter from its true value as the basis of estimating individual parametric errors. The proposed Signature Isolation Method (PARSIM) then uses these estimates to adapt individual model parameters independently of the others, implementing, in effect, concurrent adaptation of individual parameters by the Newton-Raphson method in the time-scale plane. The Signature Isolation Method has been found to have an adaptation precision comparable to that of the Gauss-Newton method for noise-free cases. However, it surpasses the Gauss-Newton method in precision when the output is contaminated with noise or when the measurements are generated by inadequate excitation. | 07-07-2011 |
20110167026 | SYSTEMS AND METHODS FOR PROVIDING EXTENSIBLE ELECTRONIC LEARNING SYSTEMS - An extensible electronic learning system having at least one learning management system having a learning management processor and a learning management memory operatively coupled thereto, said processor programmed for executing at least one learning management service and providing at least one extensible integration module. Each extensible integration module includes a predefined vendor services interface comprising at least one vendor services definition, and a vendor configuration upload component for receiving vendor configuration settings about at least one vendor. The at least one vendor having a vendor processor and a vendor memory operatively coupled thereto, said vendor processor programmed for executing a least one vendor services, the at least one of said vendor services implementing the at least one of said vendor service definition, and providing at least one vendor integration module, each vendor integration module comprising the predefined vendor services interface and the vendor configuration settings. The vendor configuration settings are received by the extensible integration module such that the learning management system may request the at least one of said vendor services based on the extensible integration module. | 07-07-2011 |
20110167027 | INFORMATION ANALYSIS APPARATUS, INFORMATION ANALYSIS METHOD, AND COMPUTER-READABLE RECORDING MEDIUM - An information analysis apparatus, an information analysis method, and a program are provided that enable target information to be determined in units of single sentences, rather than in units of plural sentences, while taking into consideration the tendency of appearance of the target information. An information analysis apparatus | 07-07-2011 |
20110173141 | METHOD AND APPARATUS FOR HYBRID TAGGING AND BROWSING ANNOTATION FOR MULTIMEDIA CONTENT - A computer program product and embodiments of systems are provided for annotating multimedia documents. The computer program product and embodiments of the systems provide for performing manual and automatic annotation. | 07-14-2011 |
20110173142 | APPARATUS AND METHODS FOR CLASSIFYING SENDERS OF UNSOLICITED BULK EMAILS - Disclosed are methods and apparatus for facilitating the filtering of unsolicited bulk electronic mail (email) sent from spammers. A plurality of recipient patterns for a plurality of emails from known spammers is logged. A plurality of recipient patterns for a plurality of emails from known non-spammers is also logged. A probabilistic model for predicting whether an unknown sender identity is a spammer is generated or modified based on the logged recipient patterns for the emails from known spammers and known non-spammers. | 07-14-2011 |
20110173143 | SYSTEM AND METHOD FOR ANALYZING EXPLORATORY BEHAVIOR - The invention provides a system and method for analyzing a subject's exploratory behavior. The system of the invention includes a tracking device configured to track motion of the subject and to generate a signal indicative of the subject's motion. A CPU analyzes the signal and identifies in the signal sequences of repeated motions, or sequences of sequences of repeated motion, for sequence of repeated motion, the CPU determines for each occurrence of the repeated motion a time at which the occurrence occurred or a time interval during which the occurrence occurred. The CPU then calculates for each occurrence of the repeated motion a value of one or more predetermined parameters of the occurrence of the motion and then calculates a time dependence of the one or more predetermined parameters during the sequence of repeated motion or the sequence of sequences of repeated motion. | 07-14-2011 |
20110178963 | SYSTEM FOR THE DETECTION OF RARE DATA SITUATIONS IN PROCESSES - An apparatus for detecting a rare situation in a process described by a plurality of parameters, the apparatus comprising: a parameter value inputter, for inputting values of at least two interrelated parameters of the plurality of parameters, the interrelated parameters constituting at least one cluster, and a rare situation detector for detecting a rare situation according to an alert policy, the alert policy being based at least on an output value of an alert model, the alert model configured to provide the output value as a function of the input parameter values of parameters constituting the at least one cluster. | 07-21-2011 |
20110178964 | Recommendation System Using Rough-Set and Multiple Features Mining Integrally and Method Thereof - The present invention solves problems of cold start, first rater, sparsity and scalability for recommendation. A recommendation system according to the present invention finds association rules through data mining. Then, the recommendation system integrates a rough-set algorithm and a statistical analysis prediction for recommendation. The recommendation is dynamically made from a result of the rough-set algorithm and a result of the statistical analysis prediction by setting a standard deviation as a threshold. | 07-21-2011 |
20110178965 | METHOD FOR TRAINING A SYSTEM TO SPECIFICALLY REACT ON A SPECIFIC INPUT - A method for training a system to specifically react on a specific input. The method can include defining a set of binary data structures, each representing a real-world component, item, or virtual object; storing each data structure as a binary pattern; creating uniquely identifiable copies of the data structures to represent individual instances of the components, items, or virtual objects; creating a virtual state space of the components, items, or virtual objects by grouping them as relevant for a specific situation; receiving an input to change a status or an attribute value of at least one of the components, items, or virtual objects; storing the received changes in a new version of the applicable data structure instance; analyzing similarities of the stored binary patterns related to a particular action performed; and if a matched binary pattern is identified, proposing at least one possible action related to the matched binary pattern. | 07-21-2011 |
20110178966 | Method for Matching Elements in Schemas of Databases Using a Bayesian Network - A method matches elements in two schemas for two associated databases using automatic schema matching (ASM), wherein there is one schema for each database, wherein the elements define objects in the databases, and wherein the matching is performed on pairs of the elements by a combined matcher including a set of matchers. A Bayesian network (BN) is constructed for the set of matchers, and for each pair of elements the following steps are performing: obtaining an individual similarity value for each pair of the elements and each matcher, determining a likelihood ratio for each individual similarity value, performing belief updating on the BN using the likelihood ratios to obtain a final similarity value and corresponding probability, and outputting the final similarity value and the probability to indicate whether the pair of the elements match, or not. | 07-21-2011 |
20110184893 | ANNOTATING QUERIES OVER STRUCTURED DATA - A query may be received at a computing device and may include one or more terms. For each set of structured data tuples, a set of tokens may be determined from the terms of the query by the computing device based on attribute values of attributes associated with the structured data tuples in the set of structured data tuples. An annotated query may be determined from each of the sets of tokens. A probability score may be determined for each of the determined annotated queries. The annotated query having the highest determined probability score may be selected, and one or more structured data tuples may be identified from the structured data tuples that have attributes with attribute values that match one or more tokens of the selected annotated query. | 07-28-2011 |
20110184894 | GENERATING A SET OF ATOMS - An automated method comprises receiving training data representing an initial data set including text representing at least one concept embodied by the data set, using the training data in order to generate a set of atoms, each atom comprising at least one word that represents one or more concepts of the initial data set, wherein generating a set of atoms comprises minimising a cost function using an iterative process to identify one or more atoms. | 07-28-2011 |
20110184895 | TRAFFIC OBJECT RECOGNITION SYSTEM, METHOD FOR RECOGNIZING A TRAFFIC OBJECT, AND METHOD FOR SETTING UP A TRAFFIC OBJECT RECOGNITION SYSTEM - A method for setting up a traffic object recognition system. A scene generator simulates three-dimensional simulations of various traffic situations which include at least one of the traffic objects. A projection unit generates signals which correspond to signals that the sensor would detect in a traffic situation simulated by the three-dimensional simulation. The signals are sent to the evaluation unit for recognizing traffic objects, and the pattern recognition is trained based on a deviation between the traffic objects simulated in the three-dimensional simulations of traffic situations and the traffic objects recognized therein. | 07-28-2011 |
20110184896 | METHOD FOR VISUALIZING FEATURE RANKING OF A SUBSET OF FEATURES FOR CLASSIFYING DATA USING A LEARNING MACHINE - A method for enhancing knowledge discovery from a dataset uses visualization of a subset features within a dataset that provide the best separation of the dataset into classes. One or more classifiers are trained using each subset of features and the success rate of the classifiers in accurately classifying the dataset is calculated. The success rate is converted into a ranking that is represented as a visually distinguishable characteristic. One or more tree structures may be displayed with a node representing each feature, and the visually distinguishable characteristic is used to indicate the scores for each feature subset. Connectors between the nodes may be used to indicate unconstrained and constrained feature sets. Nodes within a constrained path may be substituted for a feature within the preferred, unconstrained path if that feature is impractical to measure. | 07-28-2011 |
20110191273 | Evaluating ontologies - A method for providing an evaluation/verification of the correctness of an ontology is described. The method includes loading a first ontology associated with a first rule set. an extended ontology and an extended rule set are generated based at least in part on the first ontology and the first rule set. The extended rule set is applied to the extended ontology. The method also includes determining (e.g., by a data processor) a correctness of the extended ontology. Results are generated which include the correctness. Apparatus and computer readable media are also described. | 08-04-2011 |
20110191274 | Deep-Structured Conditional Random Fields for Sequential Labeling and Classification - Described is a technology by which a deep-structured (multiple layered) conditional random field model is trained and used for classification of sequential data. Sequential data is processed at each layer, from the lowest layer to a final (highest) layer, to output data in the form of conditional probabilities of classes given the sequential input data. Each higher layer inputs the conditional probability data and the sequential data jointly to output further probability data, and so forth, until the final layer which outputs the classification data. Also described is layer-by-layer training, supervised or unsupervised. Unsupervised training may process raw features to minimize average frame-level conditional entropy while maximizing state occupation entropy, or to minimize reconstruction error. Also described is a technique for back-propagation of error information of the final layer to iteratively fine tune the parameters of the lower layers, and joint training, including joint training via subgroups of layers. | 08-04-2011 |
20110191275 | SYSTEM AND METHOD TO ESTIMATE REGION OF TISSUE ACTIVATION - A computer-implemented method for determining the volume of activation of neural tissue. In one embodiment, the method uses one or more parametric equations that define a volume of activation, wherein the parameters for the one or more parametric equations are given as a function of an input vector that includes stimulation parameters. After receiving input data that includes values for the stimulation parameters and defining the input vector using the input data, the input vector is applied to the function to obtain the parameters for the one or more parametric equations. The parametric equation is solved to obtain a calculated volume of activation. | 08-04-2011 |
20110191276 | OPEN INFORMATION EXTRACTION FROM THE WEB - To implement open information extraction, a new extraction paradigm has been developed in which a system makes a single data-driven pass over a corpus of text, extracting a large set of relational tuples without requiring any human input. Using training data, a Self-Supervised Learner employs a parser and heuristics to determine criteria that will be used by an extraction classifier (or other ranking model) for evaluating the trustworthiness of candidate tuples that have been extracted from the corpus of text, by applying heuristics to the corpus of text. The classifier retains tuples with a sufficiently high probability of being trustworthy. A redundancy-based assessor assigns a probability to each retained tuple to indicate a likelihood that the retained tuple is an actual instance of a relationship between a plurality of objects comprising the retained tuple. The retained tuples comprise an extraction graph that can be queried for information. | 08-04-2011 |
20110191277 | AUTOMATIC DATA MINING PROCESS CONTROL - A data mining system includes a planning and learning module which receives as input a knowledge model and a set of goals and automatically produces as output a plurality of plans. The system includes a data mining processing unit which receives the plans as instructions and automatically creates results which are provided back to the planning and learning module as feedback. A method for data mining includes the steps of receiving as input at a planning and learning module a knowledge model and a set of goals. There is the step of automatically producing as output of the planning and learning module a plurality of plans from the input. There is the step of receiving by a data mining processing unit the plans as instructions. There is the step of automatically creating results by the data mining processing unit. There is the step of providing back to the planning and learning module the results as feedback. | 08-04-2011 |
20110202484 | ANALYZING PARALLEL TOPICS FROM CORRELATED DOCUMENTS - Access is obtained to a parallel corpus including a problem corpus and a solution corpus. A first plurality of topics are mined from the problem corpus and a second plurality of topics are mined from the solution corpus. A transition probability from the first plurality of topics to the second plurality of topics is determined, to identify a most appropriate one of the topics from the solution corpus for a given one of the topics from the problem corpus. | 08-18-2011 |
20110202485 | PCC/QOS RULE CREATION - Various exemplary embodiments relate to a method and related network node and machine-readable storage medium including one or more of the following: receiving, at the PCRN, the application request message; determining at least one requested service flow from the application request message; for each requested service flow of the at least one requested service flow, generating a new PCC rule based on the application request message; and providing each new PCC rule to a Policy and Charging Enforcement Node (PCEN). Various exemplary embodiments further include an application request message including at least one media component and at least one media subcomponent and the step of for each media subcomponent, determining a requested service flow from the media subcomponent. | 08-18-2011 |
20110202486 | Healthcare Information Technology System for Predicting Development of Cardiovascular Conditions - Described herein is a framework for predicting development of a cardiovascular condition of interest in a patient. The framework involves determining, based on prior domain knowledge relating to the cardiovascular condition of interest, a risk score as a function of patient data. The patient data may include both genetic data and non-genetic data. In one implementation, the risk score is used to categorize the patient into at least one of multiple risk categories, the multiple risk categories being associated with different strategies to prevent the onset of the cardiovascular condition. The results generated by the framework may be presented to a physician to facilitate interpretation, risk assessment and/or clinical decision support. | 08-18-2011 |
20110202487 | STATISTICAL MODEL LEARNING DEVICE, STATISTICAL MODEL LEARNING METHOD, AND PROGRAM - A statistical model learning device is provided to efficiently select data effective in improving the quality of statistical models. A data classification means | 08-18-2011 |
20110202488 | Method And Apparatus For Creating State Estimation Models In Machine Condition Monitoring - In a machine condition monitoring technique, related sensors are grouped together in clusters to improve the performance of state estimation models. To form the clusters, the entire set of sensors is first analyzed using a Gaussian process regression (GPR) to make a prediction of each sensor from the others in the set. A dependency analysis of the GPR then uses thresholds to determine which sensors are related. Related sensors are then placed together in clusters. State estimation models utilizing the clusters of sensors may then be trained. | 08-18-2011 |
20110208678 | MECHANICAL SHOCK FEATURE EXTRACTION FOR OVERSTRESS EVENT REGISTRATION - An electronic system includes an accelerometer. A method for excessive mechanical shock feature extraction for overstress event registration and cumulative tracking includes obtaining a sample from the accelerometer. Feature extraction is performed on the sample using empirical mode decomposition (EMD) to produce a plurality of modes. A pattern classifier is utilized for processing the plurality of modes to determine if the sample classifies as a shock event. If the sample classifies as a shock event, a shock event counter is incremented. If the shock event counter reaches a specified count, an indication to a user is generated. | 08-25-2011 |
20110208679 | TROUBLE PATTERN CREATING PROGRAM AND TROUBLE PATTERN CREATING APPARATUS - A computer readable, non-transitory medium has stored therein a trouble pattern creating program. The program causes a computer to execute: (a) extracting, from a plurality of log messages that are output from an information system having a plurality of configuration items and that are output in a predetermined period of time, configuration items that output the log messages; (b) calculating a degree of relationship between the configuration items extracted in the (a) extracting; (c) executing learning of the rate of the number of occurrences of troubles in the information system in the number of times the log messages are output, the learning is executed by a number of times corresponding to the degree of relationship calculated in the (b) calculating; and (d) creating, in accordance with a result of the learning in the (c) executing, a trouble pattern message that is output when a trouble occurs. | 08-25-2011 |
20110208680 | ASSISTING WITH UPDATING A MODEL FOR DIAGNOSING FAILURES IN A SYSTEM - The method includes obtaining system model data representing a set of failures in a system including a plurality of components, a set of symptoms and relationships between at least some of the failures and symptoms. The system model data is used to create a Bayesian Network. Failure cases data is also obtained, where each failure case describes the presence/absence of at least one of the symptoms and the presence/absence of at least one of the failures. A learning operation on the Bayesian Network using the failure cases data is then performed and the contribution made by at least some of the failure cases to updating the parameters of the Bayesian Network during the learning operation is assessed. Information representing the assessed contribution of the at least some failure cases is displayed. | 08-25-2011 |
20110213736 | METHOD AND ARRANGEMENT FOR AUTOMATIC CHARSET DETECTION - The invention relates, in an embodiment, to a method for handling a received document. The method includes receiving a plurality of text document samples. The method includes training, using a plurality of text document samples, to obtain a set of machine learning models. Training includes generating fundamental units from the plurality of text document samples for charsets of the plurality of text document samples. Training includes extracting a subset of said fundamental units as feature lists and converting the feature lists into a set of feature vectors. Training further includes generating the set of machine learning models from the set of feature vectors. The method includes applying the set of machine learning models against a set of target document feature vectors converted from the received document. The method includes decoding the received document to obtain decoded content of the received document based on at least the first encoding scheme. | 09-01-2011 |
20110213737 | TRAINING AND VERIFICATION USING A CORRELATED BOOSTED ENTITY MODEL - A system, method and program product training and verifying using an identity or entity model. A training system is disclosed that includes: a feature correlation system that groups features from an inputted feature data sample into subsets; a plurality of classifiers that determine if each feature classifies into an associated one of a plurality of feature models that make up the entity model; and a boosting system that boosts features from a subset for a next round of training if any of the features classify and at least one correlated feature from the subset does not classify. A verification system is disclosed that includes an identity model for the entity comprising a plurality of feature models, wherein each feature model is utilized to model a unique feature; a system for receiving a feature data sample and partitioning the feature data sample into a plurality of features; a system for determining if each of the plurality of features classifies into an associated feature model; and a voting system for analyzing a result of each attempted classification and determining an overall verification result. | 09-01-2011 |
20110213738 | METHODS AND APPARATUS TO MODEL END-TO-END CLASS OF SERVICE POLICIES IN NETWORKS - Methods and apparatus to model end-to-end class of service policies in operational networks are disclosed. An example method to generate a class of service model is described, including electronically generating a ruleset based on the class of service configuration associated with a router, electronically generating a flat representation of the ruleset, electronically generating a class of service model by composing the flat representation into a composed ruleset, and storing the class of service model in a computer-readable memory. | 09-01-2011 |
20110213739 | NON-INTRUSIVE LOAD MONITORING SYSTEM AND METHOD - In accordance with one embodiment, a system for non-intrusive load monitoring includes an output device, a data storage device including program instructions stored therein, a sensing device operably connected to a common source for a plurality of electrical devices, and an estimator operably connected to the output device, the data storage device, and the sensing device, the estimator configured to execute the program instructions to obtain data associated with a sensed state of the common source from the sensing device, obtain at least one model of each of the plurality of electrical devices, solve a Mixed Integer Programming problem for the at least one models over a fixed time horizon using the obtained data to determine a combination of operational stages of the plurality of electrical devices, and store operational data based on the solved Mixed Integer Programming problem. | 09-01-2011 |
20110213740 | SYSTEM AND METHOD FOR RESOURCE ADAPTIVE CLASSIFICATION OF DATA STREAMS - A system and method for resource adaptive classification of data streams. Embodiments of systems and methods provide classifying data received in a computer, including discretizing the received data, constructing an intermediate data structure from said received data as training instances, performing subspace sampling on said received data as test instances and adaptively classifying said received data based on statistics of said subspace sampling. | 09-01-2011 |
20110218945 | TRAINING WITH COMPLEX EVENT PROCESSING ENGINE TO IDENTIFY SEMANTIC MEANING OF VIRTUAL WORLD OBJECT STATE CHANGES - Techniques for training a system to identify state changes in objects in virtual worlds. Base events transmitted by a virtual world engine are observed. Statistical analysis of the observed base events is performed. Based at least in part on this statistical analysis, a computer processor determines that a group of one or more of the observed base events is correlated to a first identified higher-level event. Optionally, the determination is based in part on a frequency of occurrence of the group of base events, on generated rules, or both. A candidate higher-level event including the group of base events thus determined is stored. User input is received about the candidate higher-level event. If so specified by the received user input, the candidate higher-level event is stored as a second identified higher-level event. As a result, the system is advantageously trained to identify higher-level events which represent abstract situations. | 09-08-2011 |
20110218946 | PRESENTING CONTENT ITEMS USING TOPICAL RELEVANCE AND TRENDING POPULARITY - A user may request a presentation of a content item set, such as a social network comprising a set of status messages or an image database comprising a set of images. However, the volume and diversity of content items of the content item set may reduce the interest of the user in the presented content items. The potential interest of the user in the presented content items may be improved by selecting content items that are associated with one or more topics of potential interest to the user, and having a positive trending popularity among users of the content item set. Moreover, the interaction of the user with a presented content item may be monitored and used to determine the interest of the user in the topics associated with the presented content item and the popularity of the content item. | 09-08-2011 |
20110218947 | ONTOLOGICAL CATEGORIZATION OF QUESTION CONCEPTS FROM DOCUMENT SUMMARIES - Electronic documents are analyzed to identify assertions, which are inverted to generate questions that may be answered by the assertions. A document or a corpus of electronic documents may be analyzed to identify entities and relationships among entities within the text of the document(s). Assertions are identified based on the entities and relationships among the entities. Each assertion represents a fact about an entity, and a group of assertions represents a summary of the document or document corpus. The assertions are inverted to generate questions that may be answered by the assertions. The questions may be further analyzed to identify relevant concepts and topics and to cluster the questions around the concepts and topics. A combined graph may also be generated that facilitates traversal among topics, concepts, questions, assertions, document summaries, and documents. | 09-08-2011 |
20110218948 | METHODS FOR DETECTING SPAMMERS AND CONTENT PROMOTERS IN ONLINE VIDEO SOCIAL NETWORKS - The present invention relates to a method for detecting video spammers and promoters in online video social systems. Using attributes based on the user's profile, the user's social behavior in the system, and the videos posted by the user as well as the target (responded) videos, the feasibility of applying a supervised learning method to identify polluters (spammers and promoters) is investigated. | 09-08-2011 |
20110218949 | METHOD OF OPTIMIZING DATA TRAINING IN SYSTEM INCLUDING MEMORY DEVICES - In one embodiment, a method of performing data training in a system including a memory controller and at least a first memory device including a group of memory banks is disclosed. The method includes providing a plurality of enabling states for the group of memory banks, wherein each enabling state is different and for each enabling state a set of the memory banks of the group is enabled and any remaining of the memory banks of the group are not enabled. The method further includes performing a first data training procedure that includes a series of first data training operations for the first memory device, each data training operation being performed for a different one of the plurality of enabling states, generating a noise profile based on the series of first data training operations, statistically analyzing the noise profile to select a reference enabling state of the group of memory banks, and performing a second data training procedure for the first memory device using the reference enabling state. As a result, the operating speed and reliability of the system including the memory device may be improved. | 09-08-2011 |
20110218950 | METHOD, SYSTEM, AND COMPUTER-ACCESSIBLE MEDIUM FOR CLASSIFICATION OF AT LEAST ONE ICTAL STATE - An exemplary methodology, procedure, system, method and computer-accessible medium can be provided for receiving physiological data for the subject, extracting one or more patterns of features from the physiological data, and classifying the at least one state of the subject using a spatial structure and a temporal structure of the one or more patterns of features, wherein at least one of the at least one state is an ictal state. | 09-08-2011 |
20110218951 | SYSTEM AND METHOD FOR PUSHING DATA TO A MOBILE DEVICE - A method for handling information requests from mobile devices includes a memory, a state prediction module, and a push module. The memory is operable to store data requests received from the mobile devices. The state prediction module is operable to access the memory to predict forecasted data requests for a mobile device based on the stored data requests. The push module is operable to receive the forecasted data requests from the state prediction module and in response request and receive response data related to the forecasted data requests and prepare the response data for transmission to the mobile device over a wireless network. | 09-08-2011 |
20110218952 | SOUND IDENTIFICATION SYSTEMS - We describe a digital sound identification system, the system comprising: non-volatile memory for storing a Markov model; stored program memory storing processor control code; a sound data input; a processor coupled to said sound data input, to said working memory, and to said stored program memory for executing said processor control code, and wherein said processor control code comprises code to: input, from said sound data input, first sample sound data for a first sound to be identified, said first sample sound data defining first sample frequency domain data, said first sample frequency domain data defining an energy of said first sample in a plurality of frequency ranges; generate a first set of mean and variance values for at least a first Markov model of said first sample sound from said first sample frequency domain data; store said first Markov model in said non-volatile memory; input interference sound data defining interference frequency domain data; adjust said mean and variance values of said first Markov model using said interference frequency domain data; input third sound data defining third sound frequency domain data; determine a probability of said third sound frequency domain data fitting at least said first Markov model; and output sound identification data dependent on said probability. | 09-08-2011 |
20110225107 | SEMANTICS UPDATE AND ADAPTIVE INTERFACES IN CONNECTION WITH INFORMATION AS A SERVICE - Additional semantic information that describes data sets is inferred in response to a request for data from the data sets, e.g., in response to a query over the data sets, including analyzing a subset of results extracted based on the request for data to determine the additional semantic information. The additional semantic information can be verified by the publisher as correct, or satisfy correctness probabilistically. Mapping information based on the additional semantic information can be maintained and updated as the system learns additional semantic information (e.g., information about what a given column represents and data types represented), and the form of future data requests (e.g., URL based queries) can be updated to more closely correspond to the updated additional semantic information. | 09-15-2011 |
20110225108 | TEMPORAL MEMORY USING SPARSE DISTRIBUTED REPRESENTATION - A processing node in a temporal memory system includes a spatial pooler and a sequence processor. The spatial pooler generates a spatial pooler signal representing similarity between received spatial patterns in an input signal and stored co-occurrence patterns. The spatial pooler signal is represented by a combination of elements that are active or inactive. Each co-occurrence pattern is mapped to different subsets of elements of an input signal. The spatial pooler signal is fed to a sequence processor receiving and processed to learn, recognize and predict temporal sequences in the input signal. The sequence processor includes one or more columns, each column including one or more cells. A subset of columns may be selected by the spatial pooler signal, causing one or more cells in these columns to activate. | 09-15-2011 |
20110231347 | Named Entity Recognition in Query - Named Entity Recognition in Query (NERQ) involves detection of a named entity in a given query and classification of the named entity into one or more predefined classes. The predefined classes may be based on a predefined taxonomy. A probabilistic approach may be taken to detecting and classifying named entities in queries, the approach using either query log data or click through data and Weakly Supervised Latent Dirichlet Allocation (WS-LDA) to construct and train a topic model. | 09-22-2011 |
20110231348 | Regularized Dual Averaging Method for Stochastic and Online Learning - Described is a technology by which a learned mechanism is developed by solving a minimization problem by using regularized dual averaging methods to provide regularized stochastic learning and online optimization. An objective function sums a loss function of the learning task and a regularization term. The regularized dual averaging methods exploit the regularization structure in an online learning environment, in a manner that obtains desired regularization effects, e.g., sparsity under L1-regularization. | 09-22-2011 |
20110231349 | SYSTEMS AND METHODS OF COGNITIVE PATTERNS KNOWLEDGE GENERATION - A processor based system and method of generating cognitive pattern knowledge of a sensory input is disclosed. The method comprising the steps of receiving sensory input to create at least one concrete pattern, receiving at least one abstract pattern comprising abstract segments and vertically blending the concrete pattern with the abstract pattern by selectively projecting abstract segments to create a vertically blended pattern whereby the vertically blended pattern represents cognitive pattern knowledge of the sensory input. In some embodiments, the systems and methods further comprise creating a measure of a degree of vertical blending and when the measure of the degree of vertical blending exceeds a threshold, horizontally blending at least two abstract patterns to create a horizontally blended abstract pattern. | 09-22-2011 |
20110231350 | ACTIVE METRIC LEARNING DEVICE, ACTIVE METRIC LEARNING METHOD, AND ACTIVE METRIC LEARNING PROGRAM - An active metric learning device includes a metric application data analysis unit, a metric optimization unit, and an attribute clustering unit. The metric application data analysis unit is formed with a metric applying module for calculating the distance between data to be analyzed, a data analyzing module for analyzing the data using a predetermined function and the distances between the data to be analyzed and outputting the result of the data analysis, and an analysis result storage unit for storing the result of the data analysis. The metric optimization unit is formed with a feedback converting module for creating side information according to the command of feedback from the user and a metric learning module for generating a metric matrix optimized under a predetermined condition using the created side information. The attribute clustering unit clusters the metric matrix optimized by the metric optimization unit and structuralizes the attributes. | 09-22-2011 |
20110231351 | Feedback in Group Based Hierarchical Temporal Memory System - A Hierarchical Temporal Memory (HTM) network has at least first nodes and a second node at a higher level than the first nodes. The second node provides an inter-node feedback signal to the first nodes for grouping patterns and sequences (or co-occurrences) in input data received at the first nodes at the first nodes. The second node collects forward signals from the first nodes; and thus, the second node has information about the grouping of the patterns and sequences (or co-occurrences) at the first nodes. The second node provides inter-node feedback signals to the first nodes based on which the first nodes may perform the grouping of the patterns and sequences (or co-occurrences) at the first nodes. | 09-22-2011 |
20110238603 | System and Method for Predicting Events Via Dynamic Ontologies - Disclosed is a system and method for determining the probability of an event occurring. The method involves developing models relating a number of factors and variables. The factors and variables can be unique to a specified field of endeavor, such as military security or epidemiology. The models can be ontological models. A rule set is then utilized to relate certain variables in the models to a specific event. The rule set can be embodied in a computer model, such as a Bayesian-Network. The system permits a user to query a knowledge store or database to acquire referent values for the rule set. Thereafter, the referent values are used to populate the rule set and compute the probability of the event occurring. | 09-29-2011 |
20110238604 | COMPUTER-AIDED DIAGNOSTIC SYSTEMS AND METHODS FOR DETERMINING SKIN COMPOSITIONS BASED ON TRADITIONAL CHINESE MEDICINAL (TCM) PRINCIPLES - Computer-aided systems and methods are provided for determining the skin composition of a specific user according to Traditional Chinese Medicinal (TCM) principles by statistically analyzing biological and/or psychological information collected from such user, such as age, gender, bodily sensation, skin condition and complexion, sleep pattern, dietary habits, energy level, stress level, physical fitness and emotional wellness, so as to classify the skin composition of the user according to TCM principles but without employing a TCM practitioner. Preferably, the skin composition classification is indicative of Yin-Yang balance of the skin of the user or the lack thereof. The present systems and methods may further recommend to the user one or more topical skin care regimens and/or ingestible skin benefit products suitable for the skin composition of the specific user. | 09-29-2011 |
20110238605 | Information processing apparatus, information processing method, and program - An information processing apparatus including: a label acquisition section that acquires a label assigned by a user to a content selected among plural contents; a user certainty factor setting section that sets a user certainty factor to the label assigned by the user; a label prediction learning section that performs label prediction learning; a label prediction section that predicts a label regarding a content to which the label is not assigned, and calculates a label certainty factor that refers to certainty of the predicted label; a user certainty factor prediction section that performs user certainty factor prediction learning, and predicts a user certainty factor of (regarding) the predicted label of (regarding) the content to which the label is not assigned; and a selection section that selects a content to be next assigned a label among contents to which labels are not assigned. | 09-29-2011 |
20110238606 | KERNEL REGRESSION SYSTEM, METHOD, AND PROGRAM - In training data, a similarity matrix is generated for each of types of data corresponding to different kernels, and graph Laplacians are formed individually from the similarity matrices. An entire graph Laplacian is defined as linear combination of the individual graph Laplacians with coupling constants. Observation variables and latent variables associated therewith are assumed to form normal distributions, and the coupling constants are assumed to form a gamma distribution. Then, on the basis of a variational Bayesian method, a variance of the observation variables and the coupling constants can be figured out with a reasonable computational cost. Once the variance of the observation variables and the coupling constants are figured out, a predictive distribution for any input data can be figured out by means of a Laplace approximation. | 09-29-2011 |
20110246400 | SYSTEM FOR OPTICAL METROLOGY OPTIMIZATION USING RAY TRACING - Provided is a system for determining profile parameters of a sample structure on a workpiece using an optical metrology system optimized to achieve one or more accuracy targets. The optical metrology system comprises an optical metrology tool configured to measure a diffraction signal off a sample structure, an optical metrology tool model configured to model the optical metrology tool using a selected number of rays and selected beam propagation parameters for the illumination beam and the diffraction beam; a signal adjuster configured to adjust the measured diffraction signal off the sample structure using the optical metrology tool model and calibration parameters, the signal adjuster generating an adjusted metrology output signal; and a profile extractor configured to determine one or more profile parameters of the sample structure using the adjusted metrology output signal, a profile model of the sample structure, and one or more extraction modules. | 10-06-2011 |
20110246401 | GRAPHICAL INFORMATION NAVIGATOR - Embodiments are disclosed for facilitating graphical navigation of data. In a specific embodiment, the system includes a graphical user interface that is adapted to graphically depict data via one or more displayed icons. The graphical user interface is further adapted to enable a user to cause display a first icon and one or more second icons associated therewith by selection of the first icon. A learning module is adapted to monitor use of the graphical user interface and to adjust behavior of the graphical user interface in response thereto based on learned information obtained from monitoring the use of the graphical user interface. The system may be specifically adapted to facilitate user navigation of data that is maintained by Enterprise Resource Planning (ERP) software. | 10-06-2011 |
20110251980 | Interactive Optimization of the Behavior of a System - An interactive tool is described for modifying the behavior of a system, such as, but not limited to, the behavior of a classification system. The tool uses an interface mechanism to present a current global state of the system. The tool accepts one or more refinements to this global state, e.g., by accepting individual changes to parameter settings that are presented by the interface mechanism. Based on this input, the tool computes and displays the global implications of the updated parameter settings. The process of iterating over one or more cycles of user updates, followed by computation and display of the implications of the attempted refinements, has the effect of advancing the system towards a global state that exhibits desirable behavior. | 10-13-2011 |
20110251981 | Enhanced Learning and Recognition Operations for Radial Basis Functions - Methods, apparatuses and systems directed to pattern identification and pattern recognition. In some particular implementations, the invention provides a flexible pattern recognition platform including pattern recognition engines that can be dynamically adjusted to implement specific pattern recognition configurations for individual pattern recognition applications. In some implementations, the present invention also provides for a partition configuration where knowledge elements can be grouped and pattern recognition operations can be individually configured and arranged to allow for multi-level pattern recognition schemes. | 10-13-2011 |
20110258148 | ACTIVE PREDICTION OF DIVERSE SEARCH INTENT BASED UPON USER BROWSING BEHAVIOR - Many search engines attempt to understand and predict a user's search intent after the submission of search queries. Predicting search intent allows search engines to tailor search results to particular information needs of the user. Unfortunately, current techniques passively predict search intent after a query is submitted. Accordingly, one or more systems and/or techniques for actively predicting search intent from user browsing behavior data are disclosed herein. For example, search patterns of a user browsing a web page and shortly thereafter performing a query may be extracted from user browsing behavior. Queries within the search patterns may be ranked based upon a search trigger likelihood that content of the web page motivated the user to perform the query. In this way, query suggestions having a high search trigger likelihood and a diverse range of topics may be generated and/or presented to users of the web page. | 10-20-2011 |
20110258149 | RANKING SEARCH RESULTS USING CLICK-BASED DATA - Methods and computer-storage media having computer-executable instructions embodied thereon that facilitate generating a machine-learned model for ranking search results using click-based data are provided. Data is referenced from user queries, which may include search results generated by general search engines and vertical search engines. A training set is generated from the search results and click-based judgments are associated with the search results in the training set. Based on click-based judgments, identifiable features are determined from the search results in a training set. Based on determining identifiable features in a training set, a rule set is generated for ranking subsequent search results. | 10-20-2011 |
20110258150 | SYSTEMS AND METHODS FOR TRAINING DOCUMENT ANALYSIS SYSTEM FOR AUTOMATICALLY EXTRACTING DATA FROM DOCUMENTS - A method of training a document analysis system to extract data from documents is provided. The method includes: automatically analyzing images and text features extracted from a document to associate the document with a corresponding document category; comparing the extracted text features with a set of text features associated with corresponding category of the document, in which the set of text features includes a set of characters, words, and phrases; if the extracted features are found to consist of the characters, words, and phrases belonging to the set of text features associated with the corresponding document category, storing the extracted text features as the data contained in the corresponding document; and, if the extracted text features are found to include at least one text feature that does not belong to the set of text features associated with the corresponding document category, submitting the unrecognized text features to a training phase. | 10-20-2011 |
20110258151 | System and Method for Resolving Gamma-Ray Spectra - A system for identifying radionuclide emissions is described. The system includes at least one processor for processing output signals from a radionuclide detecting device, at least one training algorithm run by the at least one processor for analyzing data derived from at least one set of known sample data from the output signals, at least one classification algorithm derived from the training algorithm for classifying unknown sample data, wherein the at least one training algorithm analyzes the at least one sample data set to derive at least one rule used by said classification algorithm for identifying at least one radionuclide emission detected by the detecting device. | 10-20-2011 |
20110258152 | CATEGORIZATION AUTOMATION - A method for categorization using multiple categories including obtaining multiple uniform resource locators (URLs) associated with the multiple categories, collecting multiple web pages identified by the multiple URLs, generating vocabulary terms based on the multiple web pages, generating an N-gram file including the multiple vocabulary terms, generating multiple classified URLs by labeling the plurality of URLs based on the multiple categories, generating multiple feature vectors by processing the classified URLs and the multiple web pages against the N-gram file, generating a categorization model by applying a machine learning algorithm to the multiple feature vectors, and loading a classifier with the categorization module and the N-gram file. | 10-20-2011 |
20110258153 | COMBINING PREDICTIVE MODELS OF FORGETTING, RELEVANCE, AND COST OF INTERRUPTION TO GUIDE AUTOMATED REMINDING - The claimed matter provides systems and/or techniques that develop or use predictive models of human forgetting to effectuate automated reminding. The system includes the use of predictive models that infer the probability that aspects of items will be forgotten, models that evaluate the relevance of recalling aspects of items in different settings, based on contextual information related to user attributes associated with the items, and models of the context-sensitive cost of interrupting users with reminders. The system can combine the probability of users forgetting aspects of an item with an assessed cost of forgetting those aspects to ascertain expected costs for not being reminded about events, compare expected costs for not being reminded with expected costs for interrupting users, and based on comparisons between expected costs for being reminded and expected costs for interrupting users regarding events, generate and deliver reminder notifications to users about items. | 10-20-2011 |
20110264609 | PROBABILISTIC GRADIENT BOOSTED MACHINES - Probabilistic gradient boosted machines are described herein. A probabilistic gradient boosted machine can be utilized to learn a function based at least in part upon sets of observations of a target attribute that is common across a plurality of entities and feature vectors that are representative of such entities. The sets of observations are assumed to accord to a distribution function in the exponential family. The learned function is utilized to generate values that are employed parameterize the distribution function, such that sets of observations can be predicted for different entities. | 10-27-2011 |
20110264610 | Address Data Learning and Registration Within a Distributed Virtual Bridge - Systems and methods to forward data frames are provided. A particular apparatus may include a plurality of server computers and a distributed virtual bridge. The distributed virtual bridge may include a plurality of bridge elements coupled to the plurality of server computers and configured to forward a data frame between the plurality of server computers. The plurality of bridge elements may further be configured to automatically learn address data associated with the data frame. A controlling bridge may be coupled to the plurality of bridge elements. The controlling bridge may include a global forwarding table that is automatically updated to include the address data and is accessible to the plurality of bridge elements. | 10-27-2011 |
20110264611 | PRESENTING AN INTERACTIVE GUIDANCE STRUCTURE IN A COLLABORATIVE ENVIRONMENT - A collaborative work environment is provided that supports collaboration among users for performance of a people service that is associated with ad-hoc activities. An information base is provided that includes information relating to responsibilities of the users and work items for the ad-hoc activities. An interactive guidance structure is presented in the collaborative environment to guide actions of the users with respect to the work items. Materials produced as a result of the actions to update the information base are collected. | 10-27-2011 |
20110264612 | Automatic Rule Discovery From Large-Scale Datasets to Detect Payment Card Fraud Using Classifiers - A set of payment card transactions including a sparse set of fraudulent transactions is normalized, such that continuously valued literals in each of the set of transactions are transformed to discrete literals. The normalized transactions are used to train a classifier, such as a neural network, such that the classifier is trained to classify transactions as fraudulent or genuine. The fraudulent transactions in the set of payment card transactions are clustered to form a set of prototype transactions. Each of the discrete literals in each of the prototype transactions is expanded using sensitivity analysis using the trained classifier as an oracle, and a rule for identifying fraudulent transactions is generated for each prototype transaction based on the transaction's respective expanded literals. | 10-27-2011 |
20110264613 | METHODS, APPARATUS AND SYSTEMS USING PROBABILISTIC TECHNIQUES IN TRENDING AND PROFILING - An embodiment of the present invention provides a mobile device, comprising a processor adapted to use probabilistic techniques in trending and profiling of a user of the mobile device's behavior in order to offer recommendations by detecting patterns in the user behavior over time and thereby enabling said mobile device to predict what the user is likely to do on a given day or what the user intends to accomplish in an action that has begun. | 10-27-2011 |
20110270787 | VERIFICATION SUPPORT COMPUTER PRODUCT, APPARATUS, AND METHOD - A non-transitory, computer-readable recording medium stores therein a verification support program that causes a computer to execute identifying from a finite state machine model related to a circuit-under-test, an input count of transitions to a transition-end state and an output count of transitions from the transition-end state; determining the transition-end state to be a record/restore subject, if the identified output transition>the identified input transition count; embedding record-instruction information causing the record/restore subject to be recorded to a database, if a first element causing transition to the record/restore subject is included in a first test scenario that is in a test scenario group related to the circuit-under-test; and embedding restore-instruction information causing the record-restore subject to be restored from the database, if a second element causing transition to the record-restore subject is included in a series of elements making up a second test scenario that is in the test scenario group. | 11-03-2011 |
20110276523 | MEASURING DOCUMENT SIMILARITY BY INFERRING EVOLUTION OF DOCUMENTS THROUGH REUSE OF PASSAGE SEQUENCES - One embodiment of the present invention provides a system for estimating document similarity. During operation, the system selects a collection of documents which includes a first set of passages, constructs a passage-sequence model based on the first set of passages, receives a new document which includes a second set of passages, and determines a sequence of operations associated with the new document in relation to the collection of documents based on the constructed passage-sequence model. | 11-10-2011 |
20110276524 | Data Structures and Apparatuses for Representing Knowledge - Data structures and apparatuses to represent knowledge are disclosed. The processes can comprise labeling elements in a knowledge signature according to concepts in an ontology and populating the elements with confidence values. The data structures can comprise knowledge signatures stored on computer-readable media. The knowledge signatures comprise a matrix structure having elements labeled according to concepts in an ontology, wherein the value of the element represents a confidence that the concept is present in an information space. The apparatus can comprise a knowledge representation unit having at least one ontology stored on a computer-readable medium, at least one data-receiving device, and a processor configured to generate knowledge signatures by comparing datasets obtained by the data-receiving devices to the ontologies. | 11-10-2011 |
20110282811 | Method and Data Processing System for Automatic Identification, Processing, Interpretation and Evaluation of Objects in the Form of Digital Data, Especially Unknown Objects - The invention relates to methods and data processing systems for the automatic identification, processing, interpretation and evaluation of objects in the form of digital data, especially unknown objects. Said methods and systems are characterised in that objects which cannot be associated with any known model are entered into a case database as unknown objects. They are then available for automatic interpretation and evaluation by means of a similarity-based method. Said unknown objects can lead to new models. The model database is thereby continuously enlarged, existing models refined, and new models learned. The model data-base can be organised evenly or hierarchically in statistical models representing higher classes and lower classes. | 11-17-2011 |
20110282812 | DYNAMIC PATTERN MATCHING OVER ORDERED AND DISORDERED DATA STREAMS - Architecture introduces a new pattern operator referred to as called an augmented transition network (ATN), which is a streaming adaptation of non-reentrant, fixed-state ATNs for dynamic patterns. Additional user-defined information is associated with automaton states and is accessible to transitions during execution. ATNs are created that directly model complex pattern continuous queries with arbitrary cycles in a transition graph. The architecture can express the desire to ignore some events during pattern detection, and can also detect the absence of data as part of a pattern. The architecture facilitates efficient support for negation, ignorable events, and state cleanup based on predicate punctuations. | 11-17-2011 |
20110282813 | SYSTEM AND METHOD FOR USING PATTERN RECOGNITION TO MONITOR AND MAINTAIN STATUS QUO - The present invention relates to a method of checking data gathered from a content source comprising: receiving initial data from the content source; training a data profiler to generate a set of trusted constraint modules, said training comprising (1) selecting constraint modules having parameters that are applicable to the initial data, (2) adjusting the parameters of the applicable constraint modules to conform with new data from the content source, (3) identifying non-stable constraint modules, and (4) generating a set of trusted constraint modules by removing the non-stable constraint modules; applying the set of trusted constraint modules to subsequently received data from the content source to determine whether the subsequently received data meets the parameters of the set of trusted constraint modules; and signaling a failure upon the subsequently received data failing to meet the parameters of the set of trusted constraint modules. | 11-17-2011 |
20110282814 | METHODS AND SYSTEMS FOR IMPLEMENTING A COMPOSITIONAL RECOMMENDER FRAMEWORK - A compositional recommender framework using modular recommendation functions is described. Each modular recommendation function can use a discrete technology, such as using clustering, a database lookup, or other means. A first recommendation function can recommend to a user items, such as books to check out, automobiles to purchase, people to date, etc. Another modular recommendation function can be daisy chained with the first to recommend items that are similar or related to the first recommended items, such as users who have also checked out the same recommended book, trailers that can be towed by the recommended automobiles, or vacations booked by people that were recommended as people to date. The modular recommendation functions can be used to build customized recommendation engines for different industries. | 11-17-2011 |
20110282815 | ASSOCIATION RULE MODULE FOR DATA MINING - A system, software module, and computer program product for performing association rule based data mining that improved performance in model building, good integration with the various databases throughout the enterprise, flexible specification and adjustment of the models being built, and flexible model arrangement and export capability. The software module for performing association rule based data mining in an electronic data processing system comprises: a model setup block operable to receive client input including information specifying a setup of a association rule data mining models, generate the model setup, generate parameters for the model setup based on the received information, a modeling algorithms block operable to select and initialize a association rule modeling algorithm based on the generated model setup, and a model building block operable to receive training data and build a association rule model using the training data and the selected association rule modeling algorithm. | 11-17-2011 |
20110289025 | LEARNING USER INTENT FROM RULE-BASED TRAINING DATA - The search intent co-learning technique described herein learns user search intents from rule-based training data and denoises and debiases this data. The technique generates several sets of biased and noisy training data using different rules. It trains each of a set of classifiers using different training data sets independently. The classifiers are then used to categorize the training data as well as any unlabeled data. The classified data confidently classified by one classifier is added to other training data sets, and the wrongly classified data is filtered out from the training data sets, so as to create an accurate training data set with which to train a classifier to learn a user's intent for submitting a search query string or targeting a user for on-line advertising based on user behavior. | 11-24-2011 |
20110289026 | Matching Offers to Known Products - A method and apparatus for electronically matching an electronic offer to structured data for a product offering is disclosed. The structure data is reviewed and a dictionary of terms for each attribute from the structure data is created. Attributes in unstructured text may be determined. Each pair of the attributes (name and value) from the unstructured data and the structured data are obtained, the attribute pairs of the structured data and the unstructured data and compared and a similarity level is calculated for the matching the attribute pairs. The structured data pair that has the highest similarity score to the unstructured data pair is selected and returned. | 11-24-2011 |
20110289027 | LEGACY SYSTEM SUPPORT - A system for adapting a legacy system to a new environment includes a method of learning the behavior of a legacy system and a method for replacing a legacy system. | 11-24-2011 |
20110289028 | PATTERN RECOGNITION DEVICE, PATTERN RECOGNITION METHOD, AND PATTERN RECOGNITION PROGRAM - A pattern recognition device executes feature selection using a feature selection table. High recognition performance is possible by dimensionally lowering an n-dimensional feature vector. A feature selecting table generating section for generating a feature selecting table in a manner so that when features with up to p | 11-24-2011 |
20110295774 | Training SVMs with Parallelized Stochastic Gradient Descent - Techniques for training a non-linear support vector machine utilizing a stochastic gradient descent algorithm are provided. The computations of the stochastic gradient descent algorithm are parallelized via a number of processors. Calculations of the stochastic gradient descent algorithm on a particular processor may be combined according to a packing strategy before communicating the results of the calculations with the other processors. | 12-01-2011 |
20110295775 | ASSOCIATING MEDIA WITH METADATA OF NEAR-DUPLICATES - Techniques for identifying near-duplicates of a media object and associating metadata of the near-duplicates with the media object are described herein. One or more devices implementing the techniques are configured to identify the near duplicates based at least on similarity attributes included in the media object. Metadata is then extracted from the near-duplicates and is associated with the media object as descriptors of the media object to enable discovery of the media object based on the descriptors. | 12-01-2011 |
20110295776 | RESEARCH MISSION IDENTIFICATION - A system and method is described herein that automatically determines if a user of a search engine is conducting a research mission and then provides one or more research tools, one or more specialized searches, one or more directed ads, and/or one or more marketplace events responsive to determining that the research mission is being conducted. The automatic provision of various events and/or tools responsive to determination of the research mission can advantageously improve the experience of the user conducting the research mission. | 12-01-2011 |
20110295777 | METHOD FOR BUILDING ADAPTIVE SOFT SENSOR - The invention discloses a method for building adaptive soft sensor. The method comprises the following steps. The input and schedule vectors are constructed, and a novel learning algorithm that uses online subtractive clustering is used to recursively update the structure and parameters of a local model network. Three rules are proposed for updating centers and local model coefficients of existing clusters, for generating new clusters and new models as well as for merging existing clusters and their corresponding models. Once verified, the online inferential model can be created to generate the predicted value of process. Thus, it does not need much memory space to process the method and can be easily applied to any other machine. | 12-01-2011 |
20110295778 | INFORMATION PROCESSING APPARATUS, INFORMATION PROCESSING METHOD, AND PROGRAM - There is provided an information processing apparatus including a data pool generation section which generates an unknown data pool, a learning sample collection section which randomly collects a plurality of learning samples from the unknown data pool, a classifier generation section which generates a plurality of classifiers using the learning samples, an output feature quantity acquisition section which associates with the data, for each piece of the data, a plurality of output values, which are obtained by inputting the data into the plurality of classifiers to identify the data, as an output feature quantity represented in an output feature quantity space different from the feature quantity space, and a classification section which classifies each piece of the data into any one of a predetermined number of the classes based on the output feature quantity. | 12-01-2011 |
20110295779 | REGULAR EXPRESSION MATCHING METHOD AND SYSTEM - The present invention discloses a regex matching method and system, and relates to the field of computer technologies. The method includes: sorting multiple regexes into several regex groups, where all regexes in one regex group include a common string, which is known as a generic string; compiling each regex group into a DFA, and setting up a correlation between the generic string of each regex group and the DFA; matching to-be-matched data streams with the generic string respectively, and using the matched generic string as a matched string; obtaining a DFA corresponding to the matched string; and performing regex matching for the to-be-matched data streams according to the DFA, and outputting a matching result. The embodiments of the present invention shorten the data loading process, decrease the time consumed by data loading, and improve the matching performance. | 12-01-2011 |
20110295780 | Apparatus and Method for Personalized Delivery of Content from Multiple Data Sources - A non-transitory computer readable storage medium includes instructions to collect explicit feedback from a user regarding user content preferences. Multiple data sources are monitored. Topics associated with the multiple data sources are classified. The importance of the topics to the user is characterized. Content is delivered to the user when a selected topic exceeds an importance threshold for the user. Implicit feedback from the user that characterizes refined user content preferences is tracked. The instructions to characterize the importance of topics evaluates the explicit feedback and the implicit feedback. | 12-01-2011 |
20110295781 | Apparatus and Method for Improved Classifier Training - A non-transitory computer readable storage medium includes instructions to maintain an original training set of labeled documents, where the labeled documents correspond to a variety of topics. A new labeled document corresponding to a new topic is received. The original training set of labeled documents is modulated such that the new labeled document is over-represented with respect to the original training set. This results in a modulated training set. A classifier is trained with the modulated training set to form a trained classifier. | 12-01-2011 |
20110295782 | Clinical Decision Model - An embodiment of the invention provides a method for determining a patient-specific probability of disease. The method collects clinical parameters from a plurality of patients to create a training database. A fully unsupervised Bayesian Belief Network model is created using data from the training database; and, the fully unsupervised Bayesian Belief Network is validated. Clinical parameters are collected from an individual patient; and, such clinical parameters are input into the fully unsupervised Bayesian Belief Network model via a graphical user interface. The patient-specific probability of disease is output from the fully unsupervised Bayesian Belief Network model and sent to the graphical user interface for use by a clinician in pre-operative planning. The fully unsupervised Bayesian Belief Network model is updated using the clinical parameters from the individual patient and the patient-specific probability of disease. | 12-01-2011 |
20110295783 | Multiple Domain Anomaly Detection System and Method Using Fusion Rule and Visualization - The present invention discloses various embodiments of multiple domain anomaly detection systems and methods. In one embodiment of the invention, a multiple domain anomaly detection system uses a generic learning procedure per domain to create a “normal data profile” for each domain based on observation of data per domain, wherein the normal data profile for each domain can be used to determine and compute domain-specific anomaly data per domain. Then, domain-specific anomaly data per domain can be analyzed together in a cross-domain fusion data analysis using one or more fusion rules. The fusion rules may involve comparison of domain-specific anomaly data from multiple domains to derive a multiple-domain anomaly score meter for a particular cross-domain analysis task. The multiple domain anomaly detection system and its related method may also utilize domain-specific anomaly indicators of each domain to derive a cross-domain anomaly indicator using the fusion rules. | 12-01-2011 |
20110302111 | MULTI-LABEL CLASSIFICATION USING A LEARNED COMBINATION OF BASE CLASSIFIERS - Multi-label classification is performed by (i) applying a set of trained base classifiers to an object to generate base classifier label prediction sets comprising subsets of a set of labels; (ii) constructing a set of second level features including at least one second level feature defined by a predetermined combination of two or more of the base classifier label prediction sets; and (iii) applying a second level classifier to label the object with a set of one or more labels comprising a subset of the set of labels, labeling being based on the set of second level features. The multi-label classifier is trained by: (iv) applying operations (i) and (ii) to labeled training objects of a set of labeled training objects to generate training metadata comprising sets of second level features for the labeled training objects; and (v) training the second level classifier using the training metadata. | 12-08-2011 |
20110302112 | FORECASTING THROUGH TIME DOMAIN ANALYSIS - Embodiments include methods, apparatus, and systems for forecasting using a time domain analysis. One embodiment is a computer implemented method that receives plural cycle lengths identified in time series data and builds a model using a time domain analysis of the time series data. The model is used to predict future events or future data points. | 12-08-2011 |
20110302113 | MONITORING RELATIONSHIPS BETWEEN DIGITAL ITEMS ON A COMPUTING APPARATUS - Systems and methods are described herein that facilitate file management on a computing device and/or across multiple computing devices. Actions of a user with respect to digital items on a computing device can be monitored and utilized to build a relationship table, wherein the relationship table comprises identities of digital items and data that describes relationships between particular digital items. Such a relationship table is analyzed to provide a user with information pertaining to relationships between digital items captured in the relationship table. Relationship tables from different devices can be merged to analyze relationships between digital items across devices. | 12-08-2011 |
20110302114 | Systems and Methods For Turbo On-Line One-Class Learning - Methods for one-class learning using support vector machines from a plurality of data batches are provided. A first support vector machine is learned from the plurality of data batches by a processor. A new data batch is received by the processor and is classified by the first support vector machine. If a non-zero loss classification occurs a new support vector machine is trained using the first support vector machine and the new data batch only. Data batches can be discarded if they are represented by the current support vector machine or after being used for training an updated support vector machine. Weighing factors applied to update the first support vector machine depend upon a parameter which is optimized iteratively. Support vectors do not need to be recalculated. A classifier is learned in a number of stages equal to the number of data batches processed on-line. | 12-08-2011 |
20110302115 | METHOD AND DEVICE FOR INFORMATION RETRIEVAL - A method of information retrieval, comprising: determining, using a microprocessor, Q generative models (λ) in accordance with Probabilistic Latent Semantic Indexing (PLSI), said Q generative models being determined in an offline training; receiving a user query (q); choosing, using said microprocessor, N generative models out of the Q generative models, with N≦Q; determining, using said microprocessor, a content item (d) based on said query and a combination of the N generative models. | 12-08-2011 |
20110302116 | DATA PROCESSING DEVICE, DATA PROCESSING METHOD, AND PROGRAM - A data processing device including a learning section which expresses user movement history data obtained as learning data as a probability model which expresses activities of a user and learns parameters of the model; a destination and stopover estimation section which estimates a destination node and a stopover node from state nodes of the probability model; a current location estimation section which inputs the user movement history data in the probability model and estimates a current location node which is equivalent to the current location of the user; a searching section which searches for a route from the current location of the user to a destination using information on the estimated destination node and stopover node and the current location node and the probability model obtained by learning; and a calculating section which calculates an arrival probability and a necessary time to the searched destination. | 12-08-2011 |
20110302117 | INTERESTINGNESS RECOMMENDATIONS IN A COMPUTING ADVICE FACILITY - The present disclosure provides a recommendation to a user through a computer-based advice facility, comprising collecting topical information, wherein the collected topical information includes an interestingness aspect; filtering the collected topical information based on the interestingness aspect; determining an interestingness rating from the collected topical information, wherein the determining is through the computer-based advice facility; and providing a user with the recommendation related to the topical information based on the interestingness rating. | 12-08-2011 |
20110307422 | EXPLORING DATA USING MULTIPLE MACHINE-LEARNING MODELS - A multiple model data exploration system and method for running multiple machine-learning models simultaneously to understand and explore data. Embodiments of the system and method allow a user to gain a greater understanding of the data and to gain new insights into their data. Embodiments of the system and method also allow a user to interactively explore the problem and to navigate different views of data. Many different classifier training and evaluation experiments are run simultaneously and results are obtained. The results are aggregated and visualized across each of the experiments to determine and understand how each example is classified for each different classifier. These results then are summarized in a variety of ways to allow users to obtain a greater understanding of the data both in terms of the individual examples themselves and features associated with the data. | 12-15-2011 |
20110307423 | DISTRIBUTED DECISION TREE TRAINING - A computerized decision tree training system may include a distributed control processing unit configured to receive input of training data for training a decision tree. The system may further include a plurality of data batch processing units, each data batch processing unit being configured to evaluate each of a plurality of split functions of a decision tree for respective data batch of the training data, to thereby compute a partial histogram for each split function, for each datum in the data batch. The system may further include a plurality of node batch processing units configured to aggregate the associated partial histograms for each split function to form an aggregated histogram for each split function for each of a subset of frontier tree nodes and to determine a selected split function for each frontier tree node by computing the split function that produces highest information gain for the frontier tree node. | 12-15-2011 |
20110307424 | DETERMINATION OF TRAINING SET SIZE FOR A MACHINE LEARNING SYSTEM - Automated determination of a number of profiles for a training data set to be used in training a machine learning system for generating target function information from modeled profile parameters. In one embodiment, a first principal component analysis (PCA) is performed on a training data set, and a second PCA is performed on a combined data set which includes the training data set and a test data set. A test data set estimate is generated based on the first PCA transform and the second PCA matrix. The size of error between the test data set and the test data set estimate is used to determine whether a number of profiles associated with the training data set is sufficiently large for training a machine learning system to generate a library of spectral information. | 12-15-2011 |
20110307425 | ORGANIZING SEARCH RESULTS - Many users make use of search engines to locate desired internet content by submitting search queries. For example, a user may search for photos, applications, websites, videos, documents, and/or information regarding people, places, and things. Unfortunately, search engines may provide a plethora of information that a user may be left to sift through to find relevant content. Accordingly, one or more systems and/or techniques for organizing search results are disclosed herein. In particular, user generated content, such as photos, may be retrieved based upon a search query. The user generated content may be grouped into clusters of user generated content having similar features. Search results of the search query may be obtained and organized based upon comparing the search results with the clusters. The organized search results and/or a table of content comprising the clusters may be presented to provide an enhanced user experience. | 12-15-2011 |
20110307426 | Personalized Health Risk Assessment For Critical Care - A method for providing a personalized health risk of a patient includes receiving training data corresponding to a plurality of patients and target data corresponding to a target patient; generating model data based on the training data according to an anomaly detection method; either determining whether the target data is anomalous with respect to the training data, or determining the extent to which the target data is anomalous with respect to the training data; and either indicating whether the target patient is at risk of the adverse outcome, or indicating the extent to which the target patient is at risk of the adverse outcome. | 12-15-2011 |
20110307427 | Molecular markers predicting response to adjuvant therapy, or disease progression, inbreast cancer - Predicting response to adjuvant therapy or predicting disease progression in breast cancer is realized by (1) first obtaining a breast cancer test sample from a subject; (2) second obtaining clinicopathological data from said breast cancer test sample; (3) analyzing the obtained breast cancer test sample for presence or amount of (a) one or more molecular markers of hormone receptor status, one or more growth factor receptor markers, (b) one or more tumor suppression/apoptosis molecular markers; and (c) one or more additional molecular markers both proteomic and non-proteomic that are indicative of breast cancer disease processes; and then (4) correlating (a) the presence or amount of said molecular markers and, with (b) clinicopathological data from said tissue sample other than the molecular markers of breast cancer disease processes. A kit of (1) a panel of antibodies; (2) one or more gene amplification assays; (3) first reagents to assist said antibodies with binding to tumor samples; (4) second reagents to assist in determining gene amplification; permits, when applied to a breast cancer patient's tumor tissue sample, (A) permits observation, and determination, of a numerical level of expression of each individual antibody, and gene amplification; whereupon (B) a computer algorithm, residing on a computer can calculate a prediction of treatment outcome for a specific treatment for breast cancer, or future risk of breast cancer progression. | 12-15-2011 |
20110307428 | Screening Information for a Coverage Model - It is disclosed to determine whether information useable for a generating/updating process that comprises generating and/or updating at least one model for a coverage area of a communication node shall be discarded or made available to said generating/updating process. | 12-15-2011 |
20110307429 | AUTOMATED CLASSIFICATION ALGORITHM COMPRISING AT LEAST ONE INPUT-INVARIANT PART - A classification algorithm is separated into one or more input-invariant parts and one or more input-dependent classification parts. Classifiable electronic data is obtained via a communication network. Using the classification algorithm, classifications of a plurality of data elements in the classifiable data are identified, where the at least one classification part incorporates user input concerning classification of at least one data element of the plurality of data elements. | 12-15-2011 |
20110313953 | Automated Classification Pipeline Tuning Under Mobile Device Resource Constraints - An architecture and techniques to enable a mobile device to efficiently classify raw sensor data into useful high level inferred data is discussed. Classification efficiency is achieved by tuning the mobile device's raw sensor data classification pipeline to attain a balance of accuracy, latency and energy suitable for mobile devices. The tuning of the classification pipeline is accomplished via a multi-pipeline tuning approach that uses Statistical Machine Learning Tools (SMLTs) and a classification cost modeler. | 12-22-2011 |
20110313954 | COMMUNITY MODEL BASED POINT OF INTEREST LOCAL SEARCH - The present disclosure describes a community model based point of interest local search platform. Specifically, logs of users that store selections while accessing a point of interest application are loaded into a database. The logs are of users that have similar demographic or other community attributes. The logs are then mined for contextual parameters, including, but not limited to time of day, day of week, distance, activity, environment, popularity, and personal preferences. The point of interest selections are then mapped to a multi-dimensional map where each dimension corresponds to a contextual parameter. Clusters are evaluated by a classifier and classes of users of the community are identified. When a user then queries the community model based point of interest local search platform, contextual parameters are submitted with the query, relevant classes identified, and the corresponding point of interest information is displayed to the user. | 12-22-2011 |
20110313955 | REAL-TIME INTELLIGENT VIRTUAL CHARACTERS WITH LEARNING CAPABILITIES - A system, method, and computer-readable instructions for real-time characters with learning capabilities. A plurality of rules are defined in a rules-based system, each of the rules defining a condition that determines a behavior of a virtual agent when the rule is triggered by the condition being satisfied so that upon triggering of multiple rules at the same time, each of the behaviors of the multiple rules whose conditions were satisfied are combined into a resultant behavior for the virtual agent. This resultant behavior is compared with a desired behavior to providing feedback in the form of rewards or punishments to each of the multiple rules based on their corresponding contribution to the resultant behavior as compared to the desired behavior. | 12-22-2011 |
20110313956 | INFORMATION PROCESSING APPARATUS, INFORMATION PROCESSING METHOD AND PROGRAM - There is provided an apparatus including an information processing apparatus, including a behavior learning unit that learns an activity model representing an activity state of a user as a probabilistic state transition model from time-series data of the user's location, and that finds a state node corresponding to a location where the user conducts activities using the user's activity model, a candidate assigning unit that assigns category candidates related to location or time to the state node, and a display unit that presents the category candidate to the user. | 12-22-2011 |
20110313957 | DATA PROCESSING APPARATUS, DATA PROCESSING METHOD AND PROGRAM - A data processing apparatus includes: a learning section which obtains parameters of a probability model; a destination and stopover estimating section which estimates a destination node corresponding to a movement destination and a stopover node corresponding to a movement stopover; a current location estimating section which inputs the movement history data of the user within a predetermined time from a current time to the probability model using the parameters obtained by learning, and estimates a current location node corresponding to a current location of the user; a searching section which searches for a route to the destination from the current location of the user; and a calculating section which calculates an arrival probability and a time to reach the searched destination. The learning section includes a known or unknown determining section, a parameter updating section, a new model generating section, and a new model combining section. | 12-22-2011 |
20110313958 | SYSTEM AND METHOD FOR EMPIRICAL ENSEMBLE-BASED VIRTUAL SENSING OF PARTICULATES - A virtual sensor system and method for the estimation of an amount or concentration of particulate matter resulting from natural or man made processes comprising two or more empirical models arranged for being trained using empirical data from the processes, for receiving one or more signal input values from one or more sensors of the processes and calculating a signal output value based on the signal input values where the signal output value represents an intermediate amount or concentration of particulate matter. Further a combination function is arranged for receiving the signal output values and continuously calculating the amount or concentration of PM. | 12-22-2011 |
20110313959 | METHOD AND SYSTEM FOR LISTING CATEGORIZATION - Embodiments of a method and system for listing categorization are disclosed. A category structure may be accessed. The category structure may include a plurality of categories for items. A set of training data may be accessed from a plurality of listings from at least one of supply data and/or demand data. The supply data may be generated from seller activity of a plurality of users in a networked system. The demand data may be generated from buyer activity of the plurality of users in the networked system. Each listing may include a category from the category structure. The set of training data may be provided to a categorization application for training. The categorization application may be capable of building listing statistics by applying a classifier to the set of training data and recommending a category from the category structure for a new listing by utilizing the listing statistics. | 12-22-2011 |
20110313960 | GRAPH PATTERN RECOGNITION INTERFACE - In some example embodiments, a system and method are illustrated as including receive pattern data that includes transaction data relating to transactions between persons. Next, the system and method may include building at least one secondary network based upon the pattern data. Additionally, the system and method may include displaying the at least one secondary network. | 12-22-2011 |
20110320386 | EXTRAPOLATING EMPIRICAL MODELS FOR CONTROL, PREDICTION, AND OPTIMIZATION APPLICATIONS - The present disclosure provides novel techniques for defining empirical models having control, prediction, and optimization modalities. The empirical models may include neural networks and support vector machines. The empirical models may include asymptotic analysis as part of the model definition as allow the models to achieve enhanced results, including enhanced high-order behaviors. The high-order behaviors may exhibit gains that are non-zero trending, which may be useful for controller modalities. | 12-29-2011 |
20110320387 | Graph-based transfer learning - Transfer learning is the task of leveraging the information from labeled examples in some domains to predict the labels for examples in another domain. It finds abundant practical applications, such as sentiment prediction, image classification and network intrusion detection. A graph-based transfer learning framework propagates label information from a source domain to a target domain via the example-feature-example tripartite graph, and puts more emphasis on the labeled examples from the target domain via the example-example bipartite graph. An iterative algorithm renders the framework scalable to large-scale applications. The framework propagates the label information to both features irrelevant to the source domain and unlabeled examples in the target domain via common features in a principled way. | 12-29-2011 |
20110320388 | System, Method and Computer Program for Pattern Based Intelligent Control, Monitoring and Automation - The present invention relates to control, monitoring, and automation. The present invention more specifically relates to pattern-based intelligent control, monitoring and automation. The invention performs pattern-based monitoring. It collects signal data from one or more signals. The signal data define signal data streams. It then transforms each of the signal data streams into trends. It also discovers patterns based on the trends within each signal data stream and/or across the signal data streams. The patterns are optionally used for diagnostics and root cause analysis, online plant monitoring and operation control, plant optimization, and other environments where a causal link or correlation may exist between related inputs, states and/or outputs. | 12-29-2011 |
20110320389 | SYSTEMS AND METHODS FOR SAFETY AND BUSINESS PRODUCTIVITY - The present invention is a safety and business productivity system having the following components. One or more cameras capture video data having attribute data, the attribute data representing importance of the cameras. One or more video analytics devices process the video data from one or more of the cameras and detect primitive video events in the video data. A correlation engine correlates two or more primitive video events from the video analytics devices weighted by the attribute data of the cameras used to capture the video data. An alerting engine generates one or more alerts and performs one or more actions based on the correlation performed by the correlation engine. | 12-29-2011 |
20120005132 | PREDICTING ESCALATION EVENTS DURING INFORMATION SEARCHING AND BROWSING - One or more techniques and/or systems are disclosed for predicting escalations in users' goals or concerns in web-based searching and browsing. One or more escalation features are extracted from a webpage. The one or more escalation features are run through a classifier trained to estimate a likelihood of escalation. An escalation likelihood result is generated from running the trained classifier using the extracted features. The escalation likelihood result can comprise an estimation that a subsequent search query will comprise an escalation when compared to a previous search query. The escalation likelihood result can also comprise an estimation that a subsequent webpage selection will comprise an escalation when compared to a previous webpage selection. | 01-05-2012 |
20120005133 | SYSTEM AND METHOD FOR MAPPING SS7 BEARER CHANNELS - A system and method for associating Signaling System 7 logical circuits and bearer channels are presented. The system may include an event detector configured to receive an SS7 signaling message on an SS7 signaling link, parse a logical circuit from the SS7 signaling message, receive an SS7 bearer channel, and detect a bearer channel event on the SS7 bearer channel. A statistical learning model block is configured to calculate a correlation confidence value between said bearer channel and said logical circuit. The method may include parsing a logical circuit ID from a signaling message on an SS7 signal link, identifying a bearer channel associated with a bearer event on a bearer circuit, and calculating a current correlation confidence value between the logical circuit ID and the bearer channel. | 01-05-2012 |
20120005134 | SPATIO-TEMPORAL LEARNING ALGORITHMS IN HIERARCHICAL TEMPORAL NETWORKS - A spatio-temporal learning node is a type of HTM node which learns both spatial and temporal groups of sensed input patterns over time. Spatio-temporal learning nodes comprise spatial poolers which are used to determine spatial groups in a set of sensed input patterns. The spatio-temporal learning nodes further comprise temporal poolers which are used to determine groups of sensed input patterns that temporally co-occur. A spatio-temporal learning network is a hierarchical network including a plurality of spatio-temporal learning nodes. | 01-05-2012 |
20120005135 | RECOGNITION DICTIONARY TRAINING METHOD, SYSTEM, AND PROGRAM - The present invention provides a recognition dictionary training method, a system, and a program for allowing a computer to function as a recognition dictionary training system that does not cause a lowered recognition performance with regard to input vectors other than training data. In a recognition dictionary training system, an initial value setting means | 01-05-2012 |
20120011082 | GOVERNANCE OF MODELING SYSTEMS - A governing modeling system maintains information associated with an analytic model. One or more policies may be defined that are associated with the analytic model and one or more instances of the analytic model. The system may monitor the analytic model and the one or more instances of the analytic model based on at least some of the information, the one or more policies associated with the analytic model and the one or more policies associated with the one or more instances of the analytic model. | 01-12-2012 |
20120011083 | Product-Centric Automatic Software Identification in z/OS Systems - A software identification manager selects a unique software module that corresponds to a single knowledge base software product. Next, the software identification manager determines that one or more software modules included in a raw inventory corresponds to the unique software module and, in turn, the software identification manager includes the single knowledge base software product into a refined group of knowledge base software products. The software identification manager then matches one of the knowledge base software products included in the refined group of knowledge base software products to one of the related raw inventory software modules. Once the software identification manager identifies a match, the software identification manager stores a module identification entry in a storage area and associates the matched raw inventory software module to the matched knowledge base software product. | 01-12-2012 |
20120011084 | SEMANTIC ENTITY MANIPULATION USING INPUT-OUTPUT EXAMPLES - Semantic entity manipulation technique embodiments are presented that generate a probabilistic program capable of manipulating character strings representing semantic entities based on input-output examples. The program can then be used to produce a desired output consistent with the input-output examples from inputs of a type included in the examples. The probabilistic program is generated based on the output of parsing, transform and formatting modules. The parsing module employs a probabilistic approach to parsing the input-output examples. The transform module identifies a weighted set of transforms that are capable of producing the output item from the input items of an input-output example to a likelihood specified by their assigned weight. The formatting module generates formatting instructions that transform selected output parts into a form specified by the output items in the input-output examples. | 01-12-2012 |
20120011085 | Systems And Methods For Identifying And Notifying Users of Electronic Content Based on Biometric Recognition - Systems and methods are disclosed for manipulating electronic multimedia content to a user. One method includes generating a plurality of biometric models, each biometric model corresponding to one of a plurality of people; receiving electronic media content over a network; extracting image or audio data from the electronic media content; detecting biometric information in the image or audio data; and calculating a probability of the electronic media content involving one of the plurality of people, based on the biometric information and the plurality of biometric models. | 01-12-2012 |
20120016817 | Predicting Life Changes of Members of a Social Networking System - To predict a life change event for a user of the social networking system, such as a change in marital status, relationship status, employment status, etc., the disclosed system generates a training set of data comprising historical data of other users who have gone through a life change event. The system uses the training set data to generate a prediction algorithm using machine learning models. Furthermore, the system inputs the user data to the prediction algorithm to retrieve a prediction of whether the user will undergo one or more life change events. The system updates the user's profile to indicate the life change event and provides advertisements to the user responsive to the prediction of one or more life change events. | 01-19-2012 |
20120016818 | Classification of Biological Samples Using Spectroscopic Analysis - A method and system is described for rapidly classifying a sample of a biological fluid, comprising obtaining a spectrum of the biological fluid in response to excitation of the sample in a specified frequency range, and applying a multivariate classifier to one or more spectral regions of the spectrum to classify the biological sample into one class in a set of classes, the classes comprising at least two disease states having similar clinical symptoms. Methods and systems for developing the classifiers are also described. In one example the classification uses a vibrational spectrometer ( | 01-19-2012 |
20120016819 | Distributed multimedia document indexing strategies - A method for a system that indexes/ranks/clusters multimedia documents using hybrids of information retrieval algorithms and the stochastic optimization techniques of evolutionary computation (EC) that optimizes parameter sets comprising of object parameters. The method creates a plurality of individual parameter sets, the parameter sets comprising information sharing system object parameters for describing a model, structure, shape, design, process, search query set, or dynamic search space to be optimized and setting the initial population as a current (static parent) population. These parameters are required to filter, organize, and index any large-scale data set—information stored on a single computer, a local area network (LAN), and a wide area network (WAN) that encompasses the whole Internet—that may consists of constantly fluctuating information content over relatively short periods of time | 01-19-2012 |
20120016820 | Stochastic search strategies for multimedia resource discovery and retrieval system - A method is described for applying distributed stochastic optimization techniques of evolutionary computation using a plurality of servers and a plurality of clients machines being connected via a computer network such as the Internet. The stochastic optimization techniques of evolutionary computation seek to optimize a populations of individuals against one or more predetermined fitness criteria when applied to solving solve the network routing problem coupled with one or more information retrieval problems. The field of evolutionary computation encompasses stochastic optimization techniques, such as randomized search strategies, in the form of evolutionary strategies (ES), evolutionary programming (EP), genetic algorithms (GA), classifier systems, evolvable hardware (EHW), and genetic programming (GP). The stochastic optimization component objectives of the multimedia resource discovery and retrieval systems includes maximization of resource utilization and of overall LAN throughput. | 01-19-2012 |
20120016821 | INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING METHOD, AND PROGRAM - There is provided a method including inputting a plurality of symbol strings and attribute information desired to be extracted from each symbol string; selecting a plurality of functions from a predetermined function group including a function for converting a symbol string into a numerical value, and generating a plurality of feature quantity functions for outputting a feature quantity from the symbol string by combining the plurality of functions; inputting each symbol string to each feature quantity function, and calculating a feature quantity corresponding to each symbol string; executing machine learning using the attribute information corresponding to each symbol string and the feature quantity corresponding to each symbol string, and generating an estimation function for estimating the attribute information from the feature quantity; and outputting the feature quantity functions and the estimation function. | 01-19-2012 |
20120016822 | INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING METHOD, AND PROGRAM - There is provided an information processing method including inputting a feature quantity vector and an objective variable corresponding to the feature quantity vector, generating a basis function for outputting a scalar quantity by mapping the feature quantity vector, mapping the feature quantity vector using the basis function and calculating the scalar quantity corresponding to the feature quantity vector, evaluating whether or not the basis function used to calculate the scalar quantity is useful for estimating the objective variable using the objective variable along with the scalar quantity and the feature quantity vector corresponding to the scalar quantity, generating an estimation function for estimating the objective variable from the scalar quantity by machine learning on the basis of the scalar quantity and the objective variable corresponding to the scalar quantity using the basis function evaluated to be useful, and outputting the estimation function. | 01-19-2012 |
20120016823 | DATA COMPRESSION METHOD FOR A CLASSIFIER - A method of classifying a sample of values related to the use of a server, comprises the steps of recording, by the server, use events in a log; configuring a classifier tool with a behavioral model formed of a weighted list of parameters; establishing the sample of values from the log and supplying it as parameters to a classifier tool, whereby the classifier tool calculates a score representative of the adequacy of the sample to a target category; reading recent use events saved in the log and aggregating them over basic time intervals; storing in a database the aggregation result obtained for each basic interval in a distinct record of a first group; aggregating, when the number of records of the first group reaches a threshold, the contents of the records of the first group in a distinct record of a second group of the database; and establishing the sample of values supplied to the classifier tool from the contents of records of the database. | 01-19-2012 |
20120016824 | METHOD FOR COMPUTER-ASSISTED ANALYZING OF A TECHNICAL SYSTEM - A method for computer-assisted analyzing of a technical system is provided. The technical system is described by a case base including multiple cases, each case including a state vector with a number of attributes, the state vector referring to an operation state of the technical system, wherein a class from a number of classes is assigned to each case, each class referring to an operation condition of the technical system. Each case is processed by extracting a local information vector depending on the classes of one or more neighboring cases in the case base, the neighboring cases being similar to the case being processed according to a neighborhood measure. Subsequently, machine learning of a classification is performed based on the extracted local information vectors of the cases in the case base, resulting in a learned adaptation function providing a class depending on a local information vector extracted for a case. | 01-19-2012 |
20120016825 | DETECTOR CONFIGURATION APPARATUS, METHOD, AND PROGRAM - A detector configuration apparatus for configuring a detector that performs detection through a plurality of detection stages with different resolutions capable of objectively determining a detection target modality type to be detected in each stage. The detector is configured to detect to which of a plurality of attribute values an attribute of an object included in input data corresponds with respect to each of a plurality of modality types. A variation amount calculation unit obtains, based on a plurality of teacher data corresponding to each modality type used for training the detector, a representative value of variation between a plurality of teacher data with respect to each modality type, and a detection stage determination unit determines in which stage of the plurality of detection stages each modality type is to be detected based on the representative value of variation between the teacher data. | 01-19-2012 |
20120023041 | SYSTEM AND METHOD FOR PREDICTIVE NETWORK MONITORING - A system and a method for at least predicting a trend toward a reduction in performance of a computer and/or a computer network. Preferably, the system and method is able to predict a trend toward a potential failure of a computer and/or a computer network. | 01-26-2012 |
20120023042 | CONFIDENCE LEVEL GENERATOR FOR BAYESIAN NETWORK - A system includes a computer implemented Bayesian diagnostic system. The diagnostic system includes an inferencing engine and a conditional probability table that forms the basis for Bayesian inferences once the diagnostic system is trained. Each inference includes a diagnosis and associated probability of the diagnosis. A confidence generator receives the inferences, and generates a confidence measure for each inference. | 01-26-2012 |
20120023043 | Estimating Probabilities of Events in Sponsored Search Using Adaptive Models - A machine-learning method for estimating probability of a click event in online advertising systems by computing and comparing an aggregated predictive model (a global model) and one or more data-wise sliced predictive models (local models). The method comprises receiving training data having a plurality of features stored in a feature set and constructing a global predictive model that estimates the probability of a click event for the processed feature set. Then, partitioning the global predictive model into one or more data-wise sliced training sets for training a local model from each of the data-wise slices, and then determining whether a particular local model estimates probability of click event for the feature set better than the global model. A given feature set may be collected from historical data, and may comprise a feature vector for a plurality of query-advertisement pairs and a corresponding indicator that represents a click on the advertisement. | 01-26-2012 |
20120023044 | Issue Resolution in Expert Networks - Techniques are provided for improved issue resolution in an expert network. For example, a method comprises the following steps. Information is extracted comprising: content of one or more historical records associated with resolutions of one or more previous issues; and transfer routing sequences indicating routes through routing entities in an expert network that the one or more previous issues passed in order to be respectively resolved;. A model is computed based on at least a portion of the extracted information, wherein the computed model statistically captures one or more ticket transfer patterns among routing entities in the expert network. One or more future issue resolution routing recommendations are determined based on at least one of the one or more ticket transfer patterns captured by the computed model. | 01-26-2012 |
20120023045 | Recommender System with Training Function Based on Non-Random Missing Data - A processing device of an information processing system is operative to obtain observed feedback data, to construct a model that accounts for both the observed feedback data and additional feedback data that is missing from the observed feedback data, to optimize one or more parameters of the model using a training objective function, and to generate a list of recommended items for a given user based on the optimized model. In illustrative embodiments, the missing feedback data comprises data that is missing not at random (MNAR), and the model comprises a matrix factorization model. The processing device may implement a recommender system comprising a training module coupled to a recommendation module. | 01-26-2012 |
20120023046 | Deducing Shadow User Profiles For Ad Campaigns - A method and a system are provided for deducing shadow user profile attributes for ad campaigns aimed at target users. In one example, the system extracts tagged data from source data. The tagged data includes label information associated with an actual profile for a user. The tagged data is associated with the user. The system prepares the tagged data by splitting the tagged data into datasets, including at least training data and test data. The system generates one or more individual models based on the tagged data, wherein the one or more individual models provide the ability to deduce attributes of a profile for the user. The system then generates a composite model based on the individual models. The composite model includes a combination of the individual models that are associated with the user. The system may charge a premium for ad campaigns that are aimed at target users who are each assigned one or more shadow profile attribute values. The system may determine the premium based on the confidence level with which the one or more attribute values fits to the one or more users. The system is applicable to both display advertising and sponsored search advertising. | 01-26-2012 |
20120023047 | Method for a Pattern Discovery and Recognition - A method is for a pattern discovery and recognition, wherein a first sequence comprising first sequence symbols relating to a concept and a tag associated to the first sequence are received, transition probability matrices are obtained from transition frequency matrices representing the frequency data of the occurrences of the transitions between the first sequence symbols at different distances in the first sequence, and the transition probability matrices for each tag and each distance are learnt for obtaining an activation function determining the concept occurring in a second sequence. A computer program product and an apparatus are for executing the pattern discovery and recognition method. | 01-26-2012 |
20120030150 | Hybrid Learning Component for Link State Routing Protocols - In a network that executes a link state routing protocol, a network node receives periodic disseminations of link state information from other network nodes. The link state information includes neighboring node identity and link cost metrics. The network node calculates the initial routing paths based on the received link state information by using a link state routing algorithm. It then adapts the calculated path based on both the current link state information and past link state information through a reinforcement learning process. The network node then selects a routing path to each destination node based on the adaptation and updates the routing table accordingly. | 02-02-2012 |
20120030151 | METHOD AND SYSTEM FOR ASSESSING DATA CLASSIFICATION QUALITY - Production data classified from a data source, such as a plurality of handprinted forms, is compared to provisional truth data independently classified from the same data source for constructing master truth data. The production data is compared to the master truth data for evaluating the quality with which the production data was classified. | 02-02-2012 |
20120030152 | RANKING ENTITY FACETS USING USER-CLICK FEEDBACK - Example methods, apparatuses, or articles of manufacture are disclosed that may be implemented using one or more computing devices to facilitate or otherwise support one or more processes or operations associated with ranking entity facets using user-click feedback. | 02-02-2012 |
20120030153 | SEMICONDUCTOR SYSTEM AND DATA TRAINING METHOD THEREOF - A semiconductor system includes a semiconductor memory configured to determine whether an error has occurred in a data pattern and generate an error signal, and a memory controller configured to provide the data pattern to the semiconductor memory and perform data training with respect to the semiconductor memory using the error signal. | 02-02-2012 |
20120030154 | ESTIMATING A STATE OF AT LEAST ONE TARGET - A method of estimating a state of at least one target. The method includes obtaining at least one target measurement from a first sensor, and applying a Gaussian Process technique to a target measurement to obtain an updated target measurement. | 02-02-2012 |
20120030155 | MODEL GENERATING DEVICE AND MODEL GENERATING METHOD - A model generating device acquires event information including a time when execution of an event is started, a time when execution of the event is finished, and the type of the event. The model generating device assumes model candidates each showing a relationship between an event and another event triggered by the former event on the basis of the acquired event information, and makes a combination of the assumed model candidates. The model generating device searches the assumed model candidates to find a candidate that matches an existing model. The model generating device withdraws the model candidate searched for from the combination of the model candidates, and withdraws a candidate that is to be withdrawn from the combination of the model candidates in association with withdrawal of the model candidate found as a result of the search, thereby updating the combination of the model candidates. The model generating device thereafter generates a new model on the basis of the updated combination of the model candidates. | 02-02-2012 |
20120030156 | COMPUTER-IMPLEMENTED METHOD, CLINICAL DECISION SUPPORT SYSTEM, AND COMPUTER-READABLE NON-TRANSITORY STORAGE MEDIUM FOR CREATING A CARE PLAN - A computer-implemented method for creating a care plan for a patient, the method comprising the steps of (a) receiving an intervention goal, (b) creating a behavioral determinants list using the intervention goal, (c) assigning a ranking to each behavioral determinant in the list of behavioral determinates using patient data descriptive of the patient, (d) receiving a subset of the behavioral determinant list, wherein the subset is determined by using the ranking of each behavioral determinant in the list of behavioral determinates; and (f) creating the care plan using the subset. | 02-02-2012 |
20120036092 | METHOD AND SYSTEM FOR GENERATING A PREDICTION NETWORK - A system and method for creating a network from a number of nodes and edges, where each node is assigned data from at least one data source, the data of a data source being changeable, and wherein the data assigned to a node describe single forecasts from a prediction market, the method comprising structuring the data according to a predefined taxonomy, performing a pattern recognition within data assigned to at least two nodes, whereby the pattern recognition determines and analyzes at least two sequences of patterns of changes, comparing the sequences of patterns and deriving a correlation between the sequences of patterns from the comparison result, wherein the correlation defines the dependency between the nodes; and storing the sequences of patterns and the dependency in a pattern database, whereby the dependency forms an edge between the nodes. | 02-09-2012 |
20120036093 | Waveform Mapping Technique and Process for Tracking and Estimating Evolution of Semantic Networks - In certain embodiments, a computer-implemented method includes accessing first and second data associated with a semantic network, the first data indicating a first plurality of nodes within the semantic network and a first plurality of relationships between the first plurality of nodes at a first time, and the second data indicating a second plurality of nodes within the semantic network and a second plurality of relationships between the second plurality of nodes at a second time. The method further includes generating a first waveform from the first data and a second waveform from the second data. The waveforms indicate an activity level of each of the nodes within the semantic network. The method further includes analyzing the semantic network using the generated first and second waveforms. | 02-09-2012 |
20120036094 | LEARNING APPARATUS, IDENTIFYING APPARATUS AND METHOD THEREFOR - A learning apparatus acquires a plurality of training samples containing a plurality of attributes and known classes, gives the plurality of training samples to a route node of a decision tree to be learned as an identifier, generates a plurality of child nodes from a parent node of the decision tree, allocates the training samples whose attribute corresponding to a branch condition for classification is not a deficit values at the parent node of the decision tree out of the plurality of training samples, to any of the plurality of child nodes according to the branch condition, gives the training samples whose attribute is the deficit value, to any one of the plurality of child nodes, and executes the generation of the child nodes and the allocating of the training samples until a termination condition is satisfied. | 02-09-2012 |
20120041904 | SYSTEM AND METHOD FOR MANAGING CONTINUED ATTENTION TO DISTANCE-LEARNING CONTENT - Management of a user's continued attention to distance learning content using a general purpose computer having a central processing unit and an operating system configured to run multiple program applications concurrently. A memory stores the distance learning content. A distance learning module comprises code executable on the central processing unit, as one of the multiple program applications. The distance learning module presents the distance learning content to a user and is operable to interrupt a presentation of the distance learning content in response to prescribed events concerning another one of the multiple program applications. A method executing on a computer that an concurrently run multiple applications identifies events concerning an application other than the distance learning application, processes the identified events so as to identify a prescribed event among the identified events, and interrupts the presentation of the distance learning content in response to the prescribed event. | 02-16-2012 |
20120041905 | Supervised Nonnegative Matrix Factorization - Supervised nonnegative matrix factorization (SNMF) generates a descriptive part-based representation of data, based on the concept of nonnegative matrix factorization (NMF) aided by the discriminative concept of graph embedding. An iterative procedure that optimizes suggested formulation based on Pareto optimization is presented. The present formulation removes any dependence on combined optimization schemes. Analytical and empirical evidence is presented to show that SNMF has advantages over popular subspace learning techniques as well as current state-of-the-art techniques. | 02-16-2012 |
20120041906 | Supervised Nonnegative Matrix Factorization - Supervised kernel nonnegative matrix factorization generates a descriptive part-based representation of data, based on the concept of kernel nonnegative matrix factorization (kernel NMF) aided by the discriminative concept of graph embedding. An iterative procedure that optimizes suggested formulation based on Pareto optimization is presented. The present formulation removes any dependence on combined optimization schemes. | 02-16-2012 |
20120041907 | Suggesting Connections to a User Based on an Expected Value of the Suggestion to the Social Networking System - To suggest new connections to a user of a social networking system, the system generates a set of candidate users to whom the user has not already formed a connection. The system determines the likelihood that the user will connect to each candidate user if suggested to do so, and it also computes the value to the social networking system if the user does connect to the candidate user. Then, the system computes an expected value score for each candidate user based on the corresponding likelihood and the value. The candidate users are ranked and the suggestions are provided to the user based on the candidate users' expected value scores. The social networking system can suggest other actions to a user in addition to forming a new connection with other users. | 02-16-2012 |
20120041908 | Predictive Radiosensitivity Network Model - This invention is a model that simulates the complexity of biological signaling in a cell in response to radiation therapy. Using gene expression profiles and radiation survival assays in an algorithm, a systems model was generated of the radiosensitivity network. The network consists of ten highly interconnected genetic hubs with significant signal redundancy. The model was validated with in vitro tests perturbing network components, correctly predicting radiation sensitivity 2/3 times. The model's clinical relevance was shown by linking clinical radiosensitivity targets to the model network. Clinical applications were confirmed by testing model predictions against clinical response to preoperative radiochemotherapy in patients with rectal or esophageal cancer. | 02-16-2012 |
20120041909 | METHOD TO CONFIGURE AN IMAGING DEVICE - A database contains variants of protocols for the operation of magnetic resonance tomographs as well as different types of magnetic resonance tomographs. Each variant contains parameter values and is associated with one of the types. In a training phase, relationships are determined between the parameters among one another and/or between the parameters and the associated types and are stored as patterns in a knowledge base. A protocol plan for the operation of a new magnetic resonance tomograph is created later in an application phase using the determined pattern. The method offers the advantage that the efficiency and quality of the automatic conversion of the protocols is improved. The improved quality of the protocol plan reduces operating time and costs for a manual post-processing of the protocols. Furthermore, a higher consistency of the protocols among one another is achieved both between product families and between individual configurations. | 02-16-2012 |
20120041910 | METHOD OF ESTABLISHING A PROCESS DECISION SUPPORT SYSTEM - A method of establishing a process decision support system. Decision support systems of the kind are used in manufacturing processes, particularly industrial manufacturing processes, to monitor the performance of the processes in view of controlling the processes in order to optimise process production and quality. The method includes collecting process data of a process, collecting operational data of a process, and fusing the process data and operational data to create a fused data set (such as a consolidated rule set) of the process upon which process decisions (such as control decisions) may be taken. The process data and operational data may be fused according to methods of rules-based knowledge fusion, mathematical knowledge fusion, or case-based reasoning knowledge fusion. | 02-16-2012 |
20120041911 | COMPUTER IMPLEMENTED SYSTEM AND METHOD FOR ASSESSING A NEUROPSYCHIATRIC CONDITION OF A HUMAN SUBJECT - A method for assessing a neuropsychiatric condition (such as, but not limited to, a risk that a subject may attempt to commit suicide or repeat an attempt to commit suicide, a risk that terminally ill patient is not being cared-for or treated according to the patient's true wishes, a risk that a subject may perform or repeat a criminal act and/or a harmful act, a risk of the subject having a psychiatric illness, and/or a risk of a subject feigning a psychiatric illness) may include a plurality of steps. A step may include receiving biomarker data associated from an analysis of the subject's biological sample and a step of receiving thought-marker data obtained pertaining to one or more of the subject's recorded thoughts, spoken words, transcribed speech, and writings. A step may include generating a biomarker score associated with the neuropsychiatric condition from the biomarker data. A step may include generating a thought-marker score associated with the neuropsychiatric condition from the thought-marker data. And a step may involve calculating a neuropsychiatric condition score based, at least in part, upon the biomarker score and the thought-marker score. Such method may be operating from one or more memory devices including computer-readable instructions configured to instruct a computerized system to perform the method, and the method may be operating on a computerized system including one or more computer servers (or other available devices) accessible over a computer network such as the Internet or over some other data network. | 02-16-2012 |
20120047096 | METHOD AND APPARATUS FOR CLASSIFYING APPLICATIONS USING THE COLLECTIVE PROPERTIES OF NETWORK TRAFFIC - In one embodiment, the present disclosure is a method and apparatus for classifying applications using the collective properties of network traffic. In one embodiment, a method for classifying traffic in a communication network includes receiving a traffic activity graph, the traffic activity graph comprising a plurality of nodes interconnected by a plurality of edges, where each of the nodes represents an endpoint associated with the communication network and each of the edges represents traffic between a corresponding pair of the nodes, generating an initial set of inferences as to an application class associated with each of the edges, based on at least one measured statistic related to at least one traffic flow in the communication network, and refining the initial set of inferences based on a spatial distribution of the traffic flows, to produce a final traffic activity graph. | 02-23-2012 |
20120047097 | Secure Handling of Documents with Fields that Possibly Contain Restricted Information - A method, system and computer program product for processing documents containing restricted information. One aspect concerns identifying which sections of a document may be critical, non-critical or possibly critical. | 02-23-2012 |
20120047098 | METHOD FOR COMPUTING AND STORING VORONOI DIAGRAMS, AND USES THEREFOR - A method of producing and storing a Voronoi diagram includes: a) selecting a desired site P | 02-23-2012 |
20120059777 | CHARACTERIZING DATASETS USING SAMPLING, WEIGHTING, AND APPROXIMATION OF AN EIGENDECOMPOSITION - A method, a system, and a computer-readable medium are provided for characterizing a dataset. A representative dataset is defined from a dataset by a computing device. The representative dataset includes a first plurality of data points and the dataset includes a second plurality of data points. The number of the first plurality of data points is less than the number of the second plurality of data points. The data point is added to the representative dataset if a minimum distance between the data point and each data point of the representative dataset is greater than a sampling parameter. The data point is added to a refinement dataset if the minimum distance between the data point and each data point of the representative dataset is less than the sampling parameter and greater than half the sampling parameter. A weighting matrix is defined by the computing device that includes a weight value calculated for each of the first plurality of data points based on a determined number of the second plurality of data points associated with a respective data point of the first plurality of data points. The weight value for a closest data point of the representative dataset is updated if the minimum distance between the data point and each data point of the representative dataset is less than half the sampling parameter. A machine learning algorithm is executed by the computing device using the defined representative dataset and the defined weighting matrix applied in an approximation for a computation of a full kernel matrix of the dataset to generate a parameter characterizing the dataset. | 03-08-2012 |
20120059778 | SELF-IMPROVING CLASSIFICATION SYSTEM - A self-improving classification system classifies specimens based on class identifiers. The system stores specimen profiles in a database that is updated with additional specimen profiles and with follow-up data that corrects classification of specimens that were initially incorrectly classified. Algorithms use the updated database to discover new class identifiers, modify thresholds of known class identifiers, and drop unnecessary class identifiers to improve classification of specimens. | 03-08-2012 |
20120059779 | Personalized Health Risk Assessment For Critical Care - A method for assessing whether a patient is at risk of developing a clinical condition includes receiving training data representing a set of patient-related variables for each of a plurality of patients; generating model data based on the received training data; receiving target data representing the set of patient-related variables for a target patient; determining a risk level for the target patient of developing the clinical condition; and indicating the risk level of the target patient, where the set of patient-related variables consists of a first set of variables when the clinical condition is a mortality condition and a second set of variables when the clinical condition is a morbidity condition. | 03-08-2012 |
20120066160 | PROBABILISTIC TREE-STRUCTURED LEARNING SYSTEM FOR EXTRACTING CONTACT DATA FROM QUOTES - Systems and methods for updating data stored in a database, such as contact information. An input string is obtained through a search for timely material associated with the stored contact. The input string is parsed using probabilistic tendencies to extract entities corresponding to those stored with the contact. Secondary entities are used to assist in the identification of the primary entities. The contact is then updated (or added if new) using the extracted primary entities. | 03-15-2012 |
20120066161 | SIMPLIFIED ALGORITHM FOR ABNORMAL SITUATION PREVENTION IN LOAD FOLLOWING APPLICATIONS INCLUDING PLUGGED LINE DIAGNOSTICS IN A DYNAMIC PROCESS - Systems and methods are provided for detecting abnormal conditions and preventing abnormal situations from occurring in controlled processes. Statistical signatures of a monitored variable are modeled as a function of the statistical signatures of a load variable. The statistical signatures of the monitored variable may be modeled according to an extensible regression model or a simplified load following algorithm. The systems and methods may be advantageously applied to detect plugged impulse lines in a differential pressure flow measuring device. | 03-15-2012 |
20120072380 | REGULAR EXPRESSION MATCHING USING TCAMS FOR NETWORK INTRUSION DETECTION - A method is provided for implementing regular expression matching using ternary content-addressable memory devices. The method includes: receiving a set of regular expressions (REs) that specify data elements to be extracted from data packets; constructing a deterministic finite automaton (DFA) from the set of regular expressions; building a state transition table for each node of the deterministic finite automaton; combining the state transition tables into a single lookup table; and instantiating the lookup table in a ternary content-addressable memory device. Additional techniques are provided to reduce the TCAM space and improve RE matching speed. | 03-22-2012 |
20120072381 | Method and Apparatus for Segmenting Context Information - An approach is provided for segmenting context information. A context segmenting platform determines context information associated with a device. The context segmenting platform determines context information associated with a device. The context segmenting platform then determines one or more context patterns based, at least in part, on the context information and determines one or more transition points between the one or more context patterns. Based, at least in part, on the one or more transition points, the context segmenting platform determines to segment the context information. | 03-22-2012 |
20120078820 | MIME Technology: Using EEG brainwave data obtained via a dry or wet sensor (wired or non-wired) device to direct or otherwise influence the outcome of any media file, including but not limited to a video, video advertisement or movie file. - A method, system and process using brainwave electro-encephalographic data from any sensor to direct or influence the outcome of any media file including but not limited to video, video advertisement or movie file sequences via a media player. It uses algorithm data based upon the brainwave readings and conveys them to a media player which refers to a text based computer-scripting language file to direct the outcome or sequence of scenes in a media file playable through various platforms, including but not limited to personal computers, television, Digital Video Disc, cinema screens, handheld devices and video consoles. The computer-scripting file uses pre-defined alternative outcomes in accordance with the user's state of mind as measured by the EEG device and directs the player to play the appropriate scene or time code within a video, video advertisement or movie file, thus enabling a user to influence the progress/outcome of a file. | 03-29-2012 |
20120078821 | METHODS FOR UNSUPERVISED LEARNING USING OPTIONAL POLYA TREE AND BAYESIAN INFERENCE - The present disclosure describes an extension of the Pólya Tree approach for constructing distributions on the space of probability measures. By using optional stopping and optional choice of splitting variables, the present invention gives rise to random measures that are absolutely continuous with piecewise smooth densities on partitions that can adapt to fit the data. The resulting optional Pólya tree distribution has large support in total variation topology, and yields posterior distributions that are also optional Pólya trees with computable parameter values. | 03-29-2012 |
20120078822 | METHOD AND APPARATUS FOR PROVIDING A FRAMEWORK FOR GENERATING RECOMMEDATION MODELS - An approach is provided for providing a framework for generating recommendation models. A recommendation engine receives a request, at the recommendation engine, for generating a recommendation model for an application, wherein the recommendation engine is applicable to a plurality of applications. Next, the recommendation engine determines to retrieve rating information from on one or more profiles associated with the application, one or more other applications, or a combination thereof. Then, the recommendation engine determines to generate the recommendation model based, at least in part, on the rating information. | 03-29-2012 |
20120078823 | ABNORMALITY DIAGNOSIS FILTER GENERATOR - Provided is an apparatus determining values of N and K for an abnormality diagnostic logic which makes a diagnosis N times for each diagnosis target by using observation values collected therefrom, and generates a diagnosis result showing that the diagnosis target is abnormal if the diagnosis target is judged to be abnormal K or more times. A calculator calculates average false detection rate P | 03-29-2012 |
20120078824 | Method and System for Music Recommendation Based on Immunology - An artificial intelligence song/music recommendation system and method is provided that allows music shoppers to discover new music. The system and method accomplish these tasks by analyzing a database of music in order to identify key similarities between different pieces of music, and then recommends pieces of music to a user depending upon their music preferences. Once the song files have been analyzed and mapped, this system uses four layers, metaphorically equivalent to the human immune system, to provide music recommendation. | 03-29-2012 |
20120078825 | SEARCH RESULT RANKING USING MACHINE LEARNING - Various embodiments include systems and methods for search result ranking using machine learning. A goal model can be created using machine learning. Responsive to a search query, a plurality of data factors can be inputted into the goal model to create a model output. Search results can be presented to a user based on the model output. | 03-29-2012 |
20120078826 | FACT CHECKING USING AND AIDING PROBABILISTIC QUESTION ANSWERING - A system, a method and a computer program product for verifying a statement are provided. The system is configured to receive a statement. The system is configured to decompose the received statement into one or more sets of question and answer pairs. The system is configured to determine a confidence value of each answer in the one or more question and answer pair sets. The system is configured to combine the determined confidence values. The combined confidence values represent a probability that the received statement is evaluated as true. | 03-29-2012 |
20120078827 | HIERARCHICAL TEMPORAL MEMORY METHODS AND SYSTEMS - Methods and systems for constructing biological-scale hierarchically structured cortical statistical memory systems utilizing fabrication technology and meta-stable switching devices. Learning content-addressable memory and statistical random access memory circuits are detailed. Additionally, local and global signal modulation of bottom-up and top-down processing for the initiation and direction of behavior is disclosed. | 03-29-2012 |
20120078828 | GAS BLOCKING DEVICE - A newly-purchased gas appliance is detected and reported to a gas administrator. There are provided a flow rate detection portion, a flow rate calculation portion, a code extraction portion, an initial code learning portion, a code maintaining portion, a code judging portion, an additional code learning portion, and an external communication portion. The code extraction portion extracts a code pattern E. The initial code learning portion gathers similar code patterns E as a gas appliance code pattern F. The code judging portion judges whether or not the code pattern E matches any of gas appliance code patterns F held by the code maintaining portion within a predetermined range. The code patterns E that have failed to match are subjected to additional identification of a gas appliance in the additional code learning portion. The gas blocking device can thereby let the additional code learning portion detect whether or not a new gas appliance has emerged and the external communication portion send a report to the gas administrator. | 03-29-2012 |
20120084235 | STRUCTURED PREDICTION MODEL LEARNING APPARATUS, METHOD, PROGRAM, AND RECORDING MEDIUM - A structured prediction model learning apparatus, method, program, and recording medium maintain prediction performance with a smaller amount of memory. An auxiliary model is introduced by defining the auxiliary model parameter set θ | 04-05-2012 |
20120084236 | Recording medium storing decision tree generating program, decision tree generation method and decision tree generating apparatus - A constraint condition DB storing a constraint condition that stipulates a structure of a decision tree is referenced, and a decision tree is generated from a case set where values of a plurality of attributes and a conclusion are associated with one another so that the structure of the decision tree, which is stipulated by the constraint condition, is satisfied. Accordingly, for example, even if a new case is added to the case set, a basic structure of the decision tree is succeeded by the constraint condition, thereby avoiding a situation where an operator needs to significantly modify the decision tree. Therefore, operations of modifying the decision tree by the operator can be reduced. | 04-05-2012 |
20120084237 | DATA PROCESSING DEVICE, DATA PROCESSING METHOD, AND PROGRAM - A data processing device includes a state value calculation unit which calculates a state value of which the value increases as much as a state with a high transition probability for each state of the state transition model, an action value calculation unit which calculates an action value, of which the value increases as a transition probability increases for each state of the state transition model and each action that the agent can perform, a target state setting unit which sets a state with great unevenness in the action value among states of the state transition model to a target state that is the target to reach by action performed by the agent, and an action selection unit which selects an action of the agent so as to move toward the target state. | 04-05-2012 |
20120084238 | System and Method to Enable Training a Machine Learning Network in the Presence of Weak or Absent Training Exemplars - Described is a system and method for training a machine learning network. The method comprises initializing at least one of nodes in a machine learning network and connections between the nodes to a predetermined strength value, wherein the nodes represent factors determining an output of the network, providing a first set of questions to a plurality of users, the first set of questions relating to at least one of the factors, receiving at least one of choices and guesstimates from the users in response to the first set of questions and adjusting the predetermined strength value as a function of the choices/guesstimates. The real and simulated examples presented demonstrate that synthetic training sets derived from expert or non-expert human guesstimates can replace or augment training data sets comprised of actual training exemplars that are too limited in size, scope, or quality to otherwise generate accurate predictions. | 04-05-2012 |
20120084239 | Methods and Systems for Constructing Bayesian Belief Networks - Methods and systems are described for simplifying a causal influence model that describes influence of parent nodes X | 04-05-2012 |
20120089542 | Consistency Maintenance of Distributed Graph Structures - The present disclosure is directed to systems and methods including retrieving a model including a plurality of objects and references between objects, receiving first user input indicating a set of first changes to the model, applying changes of the set of first changes to the model to provide a first modified model, receiving second user input indicating a set of second changes to the model, identifying a conflicting operation in the set of first changes to the set of second changes, applying one or more inverse operations to the first modified model to provide a second modified model, removing the conflicting operation from the set of first changes, defining a subset of first changes including the one or more changes after the conflicting operation, reconciling one or more changes to provide a reconciled subset of first changes, and defining an updated model. | 04-12-2012 |
20120089543 | Regulated Data Analysis System - A data analysis system is invented to analysis business data. The analysis process is regulated to increase accuracy. | 04-12-2012 |
20120095943 | SYSTEM FOR TRAINING CLASSIFIERS IN MULTIPLE CATEGORIES THROUGH ACTIVE LEARNING - A system for training classifiers in multiple categories through an active learning system, including a computer having a memory and a processor, the processor programmed to: train an initial set of m binary one-versus-all classifiers, one for each category in a taxonomy, on a labeled dataset of examples stored in a database coupled with the computer; uniformly sample up to a predetermined large number of examples from a second, larger dataset of unlabeled examples stored in a database coupled with the computer; order the sampled unlabeled examples in order of informativeness for each classifier; determine a minimum subset of the unlabeled examples that are most informative for a maximum number of the classifiers to form an active set for learning; and use editorially-labeled versions of the examples of the active set to re-train the classifiers, thereby improving the accuracy of at least some of the classifiers. | 04-19-2012 |
20120095944 | Forward Feature Selection For Support Vector Machines - In one embodiment, the present invention includes a method for training a Support Vector Machine (SVM) on a subset of features (d′) of a feature set having (d) features of a plurality of training instances to obtain a weight per instance, approximating a quality for the d features of the feature set using the weight per instance, ranking the d features of the feature set based on the approximated quality, and selecting a subset (q) of the features of the feature set based on the ranked approximated quality. Other embodiments are described and claimed. | 04-19-2012 |
20120101965 | TOPIC MODELS - Machine learning techniques may be used to train computing devices to understand a variety of documents (e.g., text files, web pages, articles, spreadsheets, etc.). Machine learning techniques may be used to address the issue that computing devices may lack the human intellect used to understand such documents, such as their semantic meaning. Accordingly, a topic model may be trained by sequentially processing documents and/or their features (e.g., document author, geographical location of author, creation date, social network information of author, and/or document metadata). Additionally, as provided herein, the topic model may be used to predict probabilities that words, features, documents, and/or document corpora, for example, are indicative of particular topics. | 04-26-2012 |
20120109858 | Search with Joint Image-Audio Queries - Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for processing joint image-audio queries. In one aspect, a method includes receiving, from a client device, a joint image-audio query including query image data and query audio data. Query image feature data is determined from the query image data. Query audio feature data is determined from the audio data. The query image feature data and the query audio feature data are provided to a joint image-audio relevance model trained to generate relevance scores for a plurality of resources, each resource including resource image data defining a resource image for the resource and text data defining resource text for the resource. Each relevance score is a measure of the relevance of corresponding resource to the joint image-audio query. Data defining search results indicating the order of the resources is provided to the client device. | 05-03-2012 |
20120109859 | Scalable Ontology Extraction - Techniques for facilitating learning of one or more ontological rules of a resource description framework database are provided. The techniques include obtaining ontology vocabulary from a resource description framework database, generating a rule hypothesis by incrementally building upon a previously learnt rule from the database by adding one or more predicates to the previously learnt rule, performing a constraint check on the generated rule hypothesis by determining compatibility with each previously learnt rule to ensure that a complete rule set including each previously learnt rule and the generated rule hypothesis is consistent, validating the rule hypothesis as a rule using one or more association rule mining techniques to determine validity of the rule hypothesis against the database, and applying the rule to the database to infer one or more facts from the database to facilitate learning of one or more additional ontological rules. | 05-03-2012 |
20120109860 | Enhanced Training Data for Learning-To-Rank - Training data is used by learning-to-rank algorithms for formulating ranking algorithms. The training data can be initially provided by human judges, and then modeled in light of user click-through data to detect probable ranking errors. The probable ranking errors are provided to the original human judges, who can refine the training data in light of this information. | 05-03-2012 |
20120109861 | INFORMATION PROCESSING APPARATUS, PROCESSING METHOD THEREFOR, AND NON-TRANSITORY COMPUTER-READABLE STORAGE MEDIUM - An information processing apparatus creates, for each of a plurality of nodes, a query to be executed for a learning pattern input to the node; inputs a plurality of learning patterns to a root node of the plurality of nodes; executes, for the learning pattern input to each node, the query created for the node; determines whether the query has been effectively executed for the individual learning pattern input to each node; distributes and inputs, to a lower node of each node, an individual learning pattern for which it has been determined in the determining that the query was effectively executed in the node; deletes a learning pattern for which it has been determined in the determining that the query was not effectively executed in each node; and stores an attribute of the learning pattern input to a terminal node of the plurality of nodes in association with the node. | 05-03-2012 |
20120109862 | USER DEVICE AND METHOD OF RECOGNIZING USER CONTEXT - A method and user device for recognizing a user context are provided. The method includes: recognizing at least one behavior generated from an object by analyzing a signal obtained by at least one sensor from among a plurality of sensors included in a user device; and recognizing a current context of the user by analyzing a pattern of the at least one behavior. According to the method, a behavior of a user of a user device such a smart phone may be analyzed in real time and an appropriate service for the behavior may be provided according to the result of the analysis. | 05-03-2012 |
20120117006 | METHOD AND APPARATUS FOR BUILDING A USER BEHAVIOR MODEL - An apparatus may include a monitoring module configured to monitor user interactions by a user with applications. A contextual characteristics determiner may determine one or more contextual characteristics relating to the user interactions, and the contextual characteristics may be categorized based on an ontology model. Thereby, a data model builder may build a user behavior model for the user based at least in part on the user interactions and the contextual characteristics. The apparatus may provide for private storage of the user behavior module. A recommendation module may issue a recommendation, which may be mapped to one of the applications, based at least in part on the user behavior model. The recommendation may be issued in response to a query from a query module. The query may include current contextual characteristics of the user and/or the apparatus. | 05-10-2012 |
20120117007 | Systems and Methods to Facilitate Local Searches via Location Disambiguation - Systems and methods use machine learning techniques to resolve location ambiguity in search queries. In one aspect, a dataset generator generates a training dataset using query logs of a search engine. A training engine applies a machine learning technique to the training dataset to generate a location disambiguation model. A location disambiguation engine uses the location disambiguation model to resolve location ambiguity in subsequent search queries. | 05-10-2012 |
20120117008 | Parallel Processing Of Data Sets - Systems, methods, and devices are described for implementing learning algorithms on data sets. A data set may be partitioned into a plurality of data partitions that may be distributed to two or more processors, such as a graphics processing unit. The data partitions may be processed in parallel by each of the processors to determine local counts associated with the data partitions. The local counts may then be aggregated to form a global count that reflects the local counts for the data set. The partitioning may be performed by a data partition algorithm and the processing and the aggregating may be performed by a parallel collapsed Gibbs sampling (CGS) algorithm and/or a parallel collapsed variational Bayesian (CVB) algorithm. In addition, the CGS and/or the CVB algorithms may be associated with the data partition algorithm and may be parallelized to train a latent Dirichlet allocation model. | 05-10-2012 |
20120117009 | CONSTRUCTING A BAYESIAN NETWORK BASED ON RECEIVED EVENTS ASSOCIATED WITH NETWORK ENTITIES - Records of events associated with network entities in a network environment are received, where the network entities are selected from hardware entities, software entities, and combinations of hardware and software entities. The records of the events are identified to identify relationships between events associated with different ones of the network entities, where the records of the events identify corresponding network entities impacted by the events. A Bayesian network is constructed based on the analyzing, wherein the constructed Bayesian network is able to make predictions regarding relationships between events associated with the network elements. | 05-10-2012 |
20120123976 | Object-Sensitive Image Search - Methods and systems for object-sensitive image searches are described herein. These methods and systems are usable for receiving a query for an image of an object and providing a ranked list of query results to the user based on a ranking of the images. The object-sensitive image searches may generate a pre-trained multi-instance learning (MIL) model trained from free training data from users sharing images at websites to identify a common pattern of the object, and/or may generate a MIL model “on the fly” trained from pseudo-positive and pseudo-negative samples of query results to identify a common pattern of the object. As such, the user is presented with query results that include images that prominently display the object near the top of the results. | 05-17-2012 |
20120123977 | INFORMATION PROCESSING APPARATUS, AND METHOD, INFORMATION PROCESSING SYSTEM, AND PROGRAM - Disclosed is an information processing apparatus including: a learning unit that learns user preference for each type in each category for classifying content items in a server; a selection unit that, based on type information indicating a recommendable type which is a type of content items recommendable by the server and a substitutable type which is a type that satisfies a predetermined condition out of the recommendable type, selects one or more recommendable types in a case where there is the recommendable type corresponding with user preference in the selected category, and selects one or more substitutable types in the selected category in a case where there is no recommendable type corresponding with user preference; and an obtaining unit that obtains a content of the selected type from the server. | 05-17-2012 |
20120123978 | Learning Tags for Video Annotation Using Latent Subtags - A tag learning module trains video classifiers associated with a stored set of tags derived from textual metadata of a plurality of videos, the training based on features extracted from training videos. Each of the tag classifiers is comprised of a plurality of subtag classifiers relating to latent subtags within the tag. The latent subtags can be initialized by clustering cowatch information relating to the videos for a tag. After initialization to identify subtag groups, a subtag classifier can be trained on features extracted from each subtag group. Iterative training of the subtag classifiers can be accomplished by identifying the latent subtags of a training set using the subtag classifiers, then iteratively improving the subtag classifiers by training each subtag classifier with the videos designated as conforming closest to that subtag. | 05-17-2012 |
20120123979 | PERSON EVALUATION DEVICE, PERSON EVALUATION METHOD, AND PERSON EVALUATION PROGRAM - A person evaluation device includes a collecting unit that collects event data in which activities performed by members are recorded; a creating unit that creates a combination of evaluation programs each of which calculates an evaluation value of a person to be evaluated in accordance with a value that is set in a predetermined item contained in evaluation items contained in the event data, a calculating unit that calculates a coverage percentage that represents a percentage that is used to calculate the evaluation value by at least one evaluation program in which event data, from among the collected event data, related to a member associated with the person to be evaluated is included in the created combination, and an output unit that outputs information related to the evaluation program included in the combination that is selected in accordance with the calculated coverage percentage. | 05-17-2012 |
20120130925 | DECOMPOSABLE RANKING FOR EFFICIENT PRECOMPUTING - Methods and computer storage media are provided for generating an algorithm used to provide preliminary rankings to candidate documents. A final ranking function that provides final rankings for documents is analyzed to identify potential preliminary ranking features, such as static ranking features that are query independent and dynamic atom-isolated components that are related to a single atom. Preliminary ranking features are selected from the potential preliminary ranking features based on many factors. Using these selected features, an algorithm is generated to provide a preliminary ranking to the candidate documents before the most relevant documents are passed to the final ranking stage. | 05-24-2012 |
20120130926 | NETWORK CLASSIFICATION LINK SYSTEM, METHOD, AND COMPUTER RECORDING MEDIUM - A network classification link system, method, and computer recording medium are presented. The system includes: an operation interface, for providing an execution item menu and a network sheet; an input unit, for obtaining a first operation signal and a second operation signal to select an operating item and a network connection option corresponding to the first operation signal and the second operation signal respectively; a storage unit, for storing a network connection class, the operating item, and the network connection option; and a processing unit, for performing learning training through the operating item and the network connection option, generating a network classification link model according to the network connection class, the operating item, and the network connection, and judging a use weight of the network classification link model to establish an automatic network link mode. | 05-24-2012 |
20120130927 | Shipping System and Method with Taxonomic Tariff Harmonization - A system, method and computer-readable medium for providing a harmonized classification code for a good based on input including a database adapted to store content including a harmonized tariff classification code module for storing a data structure representing a harmonized classification code tree, the harmonized classification code tree having one or more harmonized classification codes in which the good can be classified, a keywords module for associating and storing keyword data related to the good with one of the harmonized classification codes; and a learning module for learning keywords from the input and associating the learned key words with the one harmonized classification code for the good. | 05-24-2012 |
20120136812 | METHOD AND SYSTEM FOR MACHINE-LEARNING BASED OPTIMIZATION AND CUSTOMIZATION OF DOCUMENT SIMILARITIES CALCULATION - One embodiment of the present invention provides a system for optimizing and customizing document-similarity calculation. During operation, the system presents a collection of similar documents to a user, collects feedback on the similarity of the documents from the user, generates generic rules for calculating document similarity, and filters documents with customized similarity calculation based on the feedback provided by the user. | 05-31-2012 |
20120136813 | METHOD OF PATTERN RECOGNITION IN A SIGNAL - The invention is directed to a method for pattern recognition in a signal corresponding for example to the steering angle of a vehicle for testing tires. The method comprises three major steps, namely step a) consisting in identifying phases in the signal by detecting phase changes; step b) consisting in classifying at least some of the identified phases based on their shapes and step c) consisting in detecting the presence of predetermined patterns in the signal where each predetermined pattern corresponds to a specific sequence of classes of phases. The phase changes are determined by extrema of the signal and its first derivative. The classification of the phase is made by means of parameters of the phases, namely the length dL, the amplitude dH, and a form factor S. The definition of the different classes is adjusted in a parameter space by means of manual recognition of maneuvers. | 05-31-2012 |
20120136814 | MUSIC RECOMMENDATION METHOD AND APPARATUS - A music recommendation method may include obtaining the music belongingness function of music, which is the set of granularity of music in different dimensions, wherein the dimension is the classification of music and the granularity is the classification of the dimension; obtaining the user belongingness function of a user, which is the set of granularity indicating likes of user in different dimensions; calculating a granularity correlation function by using the music belongingness function and the user belongingness function; calculating the value of the probability function indicating likes of user for music by using the granularity correlation function and a dimension weighting coefficient; and recommending the music to the user when the value of the probability function indicating likes of user for music is greater than a preset threshold. An apparatus applying to the method comprises corresponding modules. | 05-31-2012 |
20120136815 | Display Device and Display Method - According to one embodiment, a display device includes an operation module, a display module, a recorder, a compiling module, and a display controller. The operation module receives an operation from a user. The display module displays content in accordance with the operation. The recorder measures the display time of the content that is being displayed and records the display time for each content. The compiling module compiles statistical information relating to a preference of the user based on the display time recorded for each content. The display controller displays, among contents that have been previously stored, content matching with the preference of the user indicated by the statistical information on the display module. | 05-31-2012 |
20120136816 | NETWORK ANALYSIS SYSTEM - The present invention provides a method of operating a network comprising the steps of: analysing a first datastore comprising data representing historical network performance; creating or more indices within the first datastore; creating one or more probability networks in accordance with one or more of the created indices; determining from the one or more probability networks a conditional probability associated with an alarm event; and' if the conditional probability determined is less than a threshold value, disregarding the associated alarm event; or if the conditional probability determined is greater than a threshold value, using the associated alarm event in conjunction with other historical network data to predict future alarm events. | 05-31-2012 |
20120136817 | Data processing apparatus and method for motion synthesis - A data processing apparatus is used for motion synthesis. A preprocessing unit of the data processing apparatus calculates a mixture of factor analysis (MFA) parameter by applying an energy minimized optimization algorithm to motion capture data acquired in advance and stored in a motion database (DB). When a motion probability distribution model is generated as described above, a calculating unit of the data processing apparatus synthesizes a motion corresponding to input motion sensing data by applying the input motion sensing data to the motion probability distribution model. | 05-31-2012 |
20120143788 | TOXIN DETECTION SYSTEM AND METHOD - A system and method of generating a generic binary classifier for the presence of one or more toxins in water is provided. Features are extracted from a plurality of normalized a priori data sets that include one or more control data sets that are representative of an electric cell-substrate impedance sensor (ECIS) response to water with no toxins therein, and a plurality of treatment data sets that are representative of an ECIS response to water with a toxin therein. A plurality of classifier algorithms are trained using the extracted features, and a plurality of classification models are generated from each of the trained classifier algorithms. Each of the classification models is evaluated and, based on the evaluation of each classification model, a subset thereof is selected. The selected subset of the classification models is supplied as the generic binary classifier. | 06-07-2012 |
20120143789 | CLICK MODEL THAT ACCOUNTS FOR A USER'S INTENT WHEN PLACING A QUIERY IN A SEARCH ENGINE - A method of generating training data for a search engine begins by retrieving log data pertaining to user click behavior. The log data is analyzed based on a click model that includes a parameter pertaining to a user intent bias representing the intent of a user in performing a search in order to determine a relevance of each of a plurality of pages to a query. The relevance of the pages is then converted into training data. | 06-07-2012 |
20120143790 | RELEVANCE OF SEARCH RESULTS DETERMINED FROM USER CLICKS AND POST-CLICK USER BEHAVIOR OBTAINED FROM CLICK LOGS - Data from a click log may be used to generate training data for a search engine. User click behavior and user post-click behavior may be used to assess the relevance of a page to a query. Labels for training data may be generated based on data from the click log. The labels may pertain to the relevance of a page to a query. For example, user post-click behavior that may be examined includes the amount of time that a user remains on a target page when a user clicks one of the search results. | 06-07-2012 |
20120143791 | METHOD AND APPARATUS FOR CAUSING AN APPLICATION RECOMMENDATION TO ISSUE - An apparatus may include a monitoring module configured to monitor user interactions by a user with applications. A contextual characteristics determiner may determine one or more contextual characteristics relating to the user interactions. Thereby, a data model builder may build a user behavior model for the user based at least in part on the user interactions and the contextual characteristics. The apparatus may provide for private storage of the user behavior module. A recommendation module may issue a recommendation, which may be mapped to one of the applications, based at least in part on the user behavior model. The recommendation may be issued in response to a query directed to a query module. The query may include current contextual characteristics of the user and/or the apparatus. The application recommendation may include one or more applications selected from one or more content providers, as controlled by a registrar module. | 06-07-2012 |
20120143792 | PAGE SELECTION FOR INDEXING - Some implementations provide techniques for selecting web pages for inclusion in an index. For example, some implementations apply regularization to select a subset of the crawled web pages for indexing based on link relationships between the crawled web pages, features extracted from the crawled web pages, and user behavior information determined for at least some of the crawled web pages. Further, in some implementations, the user behavior information may be used to sort a training set of crawled web pages into a plurality of labeled groups. The labeled groups may be represented in a directed graph that indicates relative priorities for being selected for indexing. | 06-07-2012 |
20120143793 | FEATURE SPECIFICATION VIA SEMANTIC QUERIES - Technology is described that includes a method of feature specification via semantic queries. The method can include the operation of obtaining a data set having an identifier for each data row and a plurality of data features for each data row. A semantic query can be received that can be applied to the dataset that is usable by a machine learning tool. A entity feature map can be supplied that has entities and associated features for use by the machine learning tool. Further, a query structure can be analyzed using the entity feature map to identify input from the dataset for the machine learning tool. | 06-07-2012 |
20120143794 | ANSWER MODEL COMPARISON - This patent application pertains to answer model comparison. One implementation can determine a first frequency at which an individual answer category appears in an individual slot on a query results page when utilizing a first model. The method can ascertain a second frequency at which the individual answer category appears in the individual slot on the query results page when utilizing a second model. The method can calibrate the second model so that the second frequency approaches the first frequency. | 06-07-2012 |
20120143795 | CROSS-TRACE SCALABLE ISSUE DETECTION AND CLUSTERING - Techniques and systems for cross-trace scalable issue detection and clustering that scale-up trace analysis for issue detection and root-cause clustering using a machine learning based approach are described herein. These techniques enable a scalable performance analysis framework for computing devices addressing issue detection, which is designed as a multiple scale feature for learning based issue detection, and root cause clustering. In various embodiments the techniques employ a cross-trace similarity model, which is defined to hierarchically cluster problems detected in the learning based issue detection via butterflies of trigram stacks. The performance analysis framework is scalable to manage millions of traces, which include high problem complexity. | 06-07-2012 |
20120143796 | GROUP VARIABLE SELECTION IN SPATIOTEMPORAL MODELING - In response to issues of high dimensionality and sparsity in machine learning, it is proposed to use a multiple output regression modeling module that takes into account information on groups of related predictor features and groups of related regressions, both given as input, and outputs a regression model with selected feature groups. Optionally, the method can be employed as a component in methods of causal influence detection, which are applied on a time series training data set representing the time-evolving content generated by community members, output a model of causal relationships and a ranking of the members according to their influence. | 06-07-2012 |
20120143797 | Metric-Label Co-Learning - Labels for unlabeled media samples may be determined automatically. Characteristics and/or features of an unlabeled media sample are detected and used to iteratively optimize a distance metric and one or more labels for the unlabeled media sample according to an algorithm. The labels may be used to produce training data for a machine learning process. | 06-07-2012 |
20120143798 | Electronic Communications Triage - Triaging electronic communications in a computing system environment can mitigate issues related to large volumes of incoming electronic communications. This can include an analysis of user-specific electronic communication data and associated behaviors to predict which communications a user is likely to deem important or unimportant. Client-side application features are exposed based on the evaluation of communication importance to enable the user to process arbitrarily large volumes of incoming communications. | 06-07-2012 |
20120143799 | Method for Selecting Features Used in Continuous-Valued Regression Analysis - A method selects features used in continuous-valued regression analysis. Training data input to the method includes features and corresponding target values, wherein the target values are continuous, and there is one target value for each feature. Each threshold value is thresholded and discretized with respect to a threshold value to produce a discretized target value. Then, categorical feature selection is applied to the features, using the discrete target values, to produces selected features. The selected values can be used in any regression analysis. | 06-07-2012 |
20120143800 | Method and System for Knowledge Pattern Search and Analysis for Selecting Microorganisms Based on Desired Metabolic Property or Biological Behavior - Methods and systems for knowledge pattern search and analysis for selecting microorganisms based on desired metabolic properties or biological behaviors are disclosed in various embodiments of the invention. In one embodiment of the invention, a computer-implemented method for selecting a purpose-specific microorganism first compiles microorganisms' profiles by linking each microorganism's methanogenic, hydrogenic, electrogenic, another metabolic property, and/or another biological behavior to genetic and chemical fingerprints of metabolic and energy-generating biological pathways. Then, based on the compiled profiles of the microorganisms, the computer-implemented method groups the microorganisms into pathway characteristics using machine-learning and pattern recognition performed on a computer system, and subsequently generates a prediction called “discovered characteristics” for a desired metabolic property or a desired biological behavior of at least one microorganism. Furthermore, a profile match score may be calculated to indicate usefulness of one or more microorganisms for renewable energy generation from biological waste materials or wastewater. | 06-07-2012 |
20120143801 | INFORMATION CLASSIFICATION DEVICE, INFORMATION CLASSIFICATION METHOD, AND COMPUTER READABLE RECORDING MEDIUM - An information classification device ( | 06-07-2012 |
20120150771 | Knowledge Corroboration - Knowledge corroboration is described. In an embodiment many judges provide answers to many questions so that at least one answer is provided to each question and at least some of the questions have answers from more than one judge. In an example a probabilistic learning system takes features describing the judges or the questions or both and uses those features to learn an expertise of each judge. For example, the probabilistic learning system has a graphical assessment component which aggregates the answers in a manner which takes into account the learnt expertise in order to determine enhanced answers. In an example the enhanced answers are used for knowledge base clean-up or web-page classification and the learnt expertise is used to select judges for future questions. In an example the probabilistic learning system has a logical component that propagates answers according to logical relations between the questions. | 06-14-2012 |
20120150772 | Social Newsfeed Triage - A social newsfeed being delivered to a user is triaged. A personalized model is established which predicts the importance to the user of data elements within a current social newsfeed being delivered to the user. The personalized model is established based on implicit actions the user takes in response to receiving previous social newsfeeds. The personalized model is then used to triage the data elements within the current social newsfeed. | 06-14-2012 |
20120150773 | USER INTERFACE AND WORKFLOW FOR PERFORMING MACHINE LEARNING - A computing device receives a training data set that includes a plurality of positive examples of sensitive data and a plurality of negative examples of sensitive data via a user interface. The computing device analyzes the training data set using machine learning to generate a machine learning-based detection (MLD) profile that can be used to classify new data as sensitive data or as non-sensitive data. The computing device displays a quality metric for the MLD profile in the user interface. | 06-14-2012 |
20120150774 | CONTROL APPARATUS - A control apparatus includes a learning portion which learns a control parameter by correcting a learning vector consisting of a plurality of variables and a control parameter based on a measurement vector. The control apparatus further includes an interpolation portion which computes the control parameter corresponding to current variables which represent a current environmental condition by interpolating the control parameter learned by the learning portion. The interpolation portion includes a selecting portion which selects three learning vectors from a plurality of learning vectors, and which computes the control parameter corresponding to the current variables by interpolating the control parameters on a flat surface including the selected three learning vectors. | 06-14-2012 |
20120150775 | SYSTEM FOR SEMANTIC HOME NETWORK MANAGEMENT, CLOUD INFERENCE APPARATUS FOR SEMANTIC HOME NETWORK MANAGEMENT, SEMANTIC HOME NETWORK, AND SEMANTIC HOME NETWORK CONNECTION DEVICE - A semantic home network management system includes: a home network for collecting sensing information based on a state of a home network and analyzing the collected sensing information to configure semantic information; and a cloud inference apparatus for collecting and managing the semantic information provided from the home network through an internet protocol (IP) network, and applying an inference rule to the collected and managed semantic information to provide inference rule-applied semantic information to the home network. The inference rule is a rule that performs inference by recognizing at least one of a device state, a network state, a system state, and a service state. | 06-14-2012 |
20120158618 | REMOTE NON-INTRUSIVE OCCUPANT SPACE MONITORING SYSTEM - A system for remote non-intrusive occupant space monitoring. The system may have sensors and other mechanisms for non-intrusively obtaining information by capturing utility and communication signals, images, light, sound, environmental factors, background information, and so on, about a space and its occupants. The obtained information may be locally or remotely analyzed and modeled by a processor. Models of buildings, behavior, and power systems from the processor may be compared with pre-defined models to infer further information about the space and its occupants. Also, behavioral information may be obtained, inferred and/or learned. The models may be updated with the obtained, inferred and learned information. | 06-21-2012 |
20120158619 | OPTIMAL RULE SET MANAGEMENT - Systems, methods, and computer products for optimally managing large rule sets are disclosed. Rule dependencies of rules within a set of rules may be determined as a function of rules execution frequency data generated from applying the rules over a data set. The rules within the set of rules may be clustered into rules clusters based on the determined rule dependencies, in which the rules clusters comprise disjoint subsets of the rules within the set of rules. Cluster frequency data for the rules clusters may be used to arrive at an optimal ordering. | 06-21-2012 |
20120158620 | HUMAN-ASSISTED TRAINING OF AUTOMATED CLASSIFIERS - Many computing scenarios involve the classification of content items within one or more categories. The content item set may be too large for humans to classify, but an automated classifier (e.g., an artificial neural network) may not be able to classify all content items with acceptable accuracy. Instead, the automated classifier may calculate a classification confidence while classifying respective content items. Content items having a low classification confidence may be sent to a human classifier, and may be added, along with the categories identified by the human classifier, to a training set. The automated classifier may then be retrained using the training set, thereby incrementally improving the classification confidence of the automated classifier while conserving the involvement of human classifiers. Additionally, human classifiers may be rewarded for classifying the content items, and the costs of such rewards may be considered while selecting content items for the training set. | 06-21-2012 |
20120158621 | STRUCTURED CROSS-LINGUAL RELEVANCE FEEDBACK FOR ENHANCING SEARCH RESULTS - A “Cross-Lingual Unified Relevance Model” provides a feedback model that improves a machine-learned ranker for a language with few training resources, using feedback from a more complete ranker for a language that has more training resources. The model focuses on linguistically non-local queries, such as “world cup” (English language/U.S. market) and “copa mundial” (Spanish language/Mexican market), that have similar user intent in different languages and markets or regions, thus allowing the low-resource ranker to receive direct relevance feedback from the high-resource ranker. Among other things, the Cross-Lingual Unified Relevance Model differs from conventional relevancy-based techniques by incorporating both query- and document-level features. More specifically, the Cross-Lingual Unified Relevance Model generalizes existing cross-lingual feedback models, incorporating both query expansion and document re-ranking to further amplify the signal from the high-resource ranker to enable a learning to rank approach based on appropriately labeled training data. | 06-21-2012 |
20120158622 | INTERACTIVE RECOMMENDATIONS - An interactive recommendation system generates one or more recommendations (e.g., recommended products, travel destinations, etc.) for a user based on a recommendation model. The recommendation model includes one or more criteria that are used to analyze a datastore of user characteristics (e.g., a user's age, location, past online behavior, etc.) and generate one or more recommendations based thereon. The interactive recommendation system further presents a user interface that allows the user to interactively modify the criteria of the recommendation model and to apply the modified recommendation model to the datastore in order to generate one or more modified recommendations. In this manner, for example, the user can customize the recommendations he or she receives by interacting with the recommendation system to modify the recommendation model used to generate such recommendations. | 06-21-2012 |
20120158623 | VISUALIZING MACHINE LEARNING ACCURACY - The claimed subject matter provides a method for visualizing machine learning accuracy. The method includes receiving a plurality of training instances for the machine learning system. The method also includes receiving a plurality of results for the machine learning system. The plurality of results corresponds to the plurality of training instances. The method further includes providing an interactive representation of the training instances and the results. The interactive representation supports identifying inaccuracies of the machine learning system attributable to the training instances, the features used to obtain a featurized form of the training instance, and/or a model implemented by the machine learning system. | 06-21-2012 |
20120158624 | PREDICTIVE MODELING - A predictive analysis generates a predictive model (Padj(Y|X)) based on two separate pieces of information,
| 06-21-2012 |
20120158625 | Creating and Processing a Data Rule - A data rule is created and processed by receiving an expression defining a logic of a rule and at least one logical variable, creating a rule definition including the expression and the at least one logical variable for binding each logical variable of the rule with at least one column, associating a characteristic enabling comparison of columns with a first logical variable of the rule definition, and storing the characteristic as part of the rule definition. | 06-21-2012 |
20120166366 | HIERARCHICAL CLASSIFICATION SYSTEM - The claimed subject matter provides a method for hierarchical classification. The method includes receiving a hierarchical structure with a first level comprising a parent node and a sibling node. The structure also includes a second level comprising two child nodes. The method further includes receiving training examples. Each training example may be associated with a class of the parent node, the sibling node, or the two child nodes. The method also includes generating a first classifier for the first level. The first classifier includes a first hyperplane distinguishing the parent and sibling nodes. A first vector is normal to the first hyperplane. Additionally, the method includes generating a second classifier for the second level. The second classifier includes a second hyperplane distinguishing the two child nodes. A second vector is normal to the second hyperplane. An orthogonality of the second vector in relation to the first vector is maximized. | 06-28-2012 |
20120166367 | LOCATING A USER BASED ON AGGREGATED TWEET CONTENT ASSOCIATED WITH A LOCATION - A user submitting a query from a computer at an unknown location is located using a language model. The language model is derived from an aggregation of tweets that were sent from known locations. | 06-28-2012 |
20120166368 | APPARATUS FOR GENERATING A PROBABILITY GRAPH MODEL USING A COMBINATION OF VARIABLES AND METHOD FOR DETERMINING A COMBINATION OF VARIABLES - An apparatus and method for generating a probability graph model are provided. When generating a probability graph model using variable combinations, a variable combination that has a small amount of information may not generated, thereby reducing the amount of computation. The apparatus may acquire independent variables including a plurality of input variables corresponding to context information and an output variable corresponding to an inference result, and may determine a variable combination that is to be generated, based on the amount of information of each of variable combinations with respect to the output value, in which the variable combination is defined based on combining of the input variables. | 06-28-2012 |
20120166369 | METHOD FOR DETERMINING HARMFUL MULTIMEDIA CONTENT USING MULTIMEDIA CONTENT PLAYBACK CHARACTERISTICS - A method for determining harmful multimedia content by using multimedia content playback characteristics includes: determining a local harmfulness of each basic unit section of multimedia content to generate a local determination result; and generating global determination results to complement an error of the local determination result based on the multimedia content playback characteristics. The global determination results are generated by using a continuous determination value which has a meaning of harmful or harmless and is updated depending on each local determination result and the number of continuous determination results is counted or initialized depending on continuity of the local determination results. | 06-28-2012 |
20120166370 | SMART ATTRIBUTE CLASSIFICATION (SAC) FOR ONLINE REVIEWS - Techniques for identifying attributes in a sentence and determining a number of attributes to be associated with the sentence is described. | 06-28-2012 |
20120173465 | Automatic Variable Creation For Adaptive Analytical Models - A system and method for automated variable creation for adaptive fraud analytics are disclosed. A data structure for creation of rules is generated. The data structure represents nodes and associations between nodes from inputs for fraud/non-fraud conditions, and is generated from fraud and non-fraud data collected in an adaptive modeling process from past transactions. All unique paths between nodes of the data structure are determined to define a rule for each path. Each rule is then converted to a binary indicator variable to generate a set of binary indicator variables, and one or more complex variables is derived from the set of binary indicator variables. The one or more binary indicator variables and one or more complex variables can be provided to an adaptive scoring engine to score new transactions or to predict future behaviors. | 07-05-2012 |
20120173466 | AUTOMATIC ANALYSIS OF LOG ENTRIES THROUGH USE OF CLUSTERING - A set of log entries is automatically inspected to determine a bug. A training set is utilized to determine clustering of log identifications. Log entries are examined in real-time or retroactively and matched to clusters. Timeframe may also be matched to a cluster based on log entries associated with the timeframe. Error indications may be outputted to a user of the system in respect to a log entry or a timeframe. | 07-05-2012 |
20120173467 | CONSTRUCTION OF AN AGENT THAT UTILIZES AS-NEEDED CANONICAL RULES - A method for constructing an agent that utilizes an as-needed canonical rule set in a first execution environment comprising requesting the as-needed rule set for the agent, supplying the agent with the as-needed rule set and requesting compilation of the as-needed rule set. | 07-05-2012 |
20120179633 | IDENTIFICATION OF ATTRIBUTES AND VALUES USING MULTIPLE CLASSIFIERS - A body of text comprises a plurality of unknown attributes and a plurality of unknown values. A first classification sub-component labels a first portion of the plurality of unknown values as a first set of values, whereas a second classification sub-component labels a portion of the plurality of unknown attributes as a set of attributes and a second portion of the plurality of unknown values as a second set of values. Learning models implemented by the first and second classification subcomponents are updated based on the set of attributes and the first and second set of values. The first classification sub-component implements at least one supervised classification technique, whereas the second classification sub-component implements an unsupervised and/or semi-supervised classification technique. Active learning may be employed to provide at least one of a corrected attribute and/or corrected value that may be used to update the learning models. | 07-12-2012 |
20120179634 | SYSTEM AND METHODS FOR FINDING HIDDEN TOPICS OF DOCUMENTS AND PREFERENCE RANKING DOCUMENTS - Systems and methods are disclosed to perform preference learning on a set of documents includes receiving raw input features from the set of documents stored on a data storage device; generating polynomial combinations from the raw input features; generating one or more parameters; applying the parameters to one or more classifiers to generate outputs; determining a loss function and parameter gradients and updating parameters determining one or more sparse regularizing terms and updating the parameters; and expressing that one document is preferred over another in a search query and retrieving one or more documents responsive to the search query. | 07-12-2012 |
20120179635 | METHOD AND SYSTEM FOR MULTIPLE DATASET GAUSSIAN PROCESS MODELING - A method of computerised data analysis and synthesis is described. First and second datasets of a quantity of interest are stored. A Gaussian process model is generated using the first and second datasets to compute optimized kernel and noise hyperparameters. The Gaussian process model is applied using the stored first and second datasets and hyperparameters to perform Gaussian process regression to compute estimates of unknown values of the quantity of interest. The resulting computed estimates of the quantity of interest result from a non-parametric Gaussian process fusion of the first and second measurement datasets. The first and second datasets may be derived from the same or different measurement sensors. Different sensors may have different noise and/or other characteristics. | 07-12-2012 |
20120185415 | SYSTEM AND METHOD FOR DOMAIN ADAPTION WITH PARTIAL OBSERVATION - System, method and computer program product provides a novel domain adaption/transfer learning approach applied to the problem of classifying abbreviated documents, e.g., short text messages, instant messages, tweets. The proposed method uses a large number of multi-labeled examples (source domain) to improve the learning on the partial observations (target domain). Specifically, a hidden, higher-level abstraction space is learned that is meaningful for the multi-labeled examples in the source domain. This is done by simultaneously minimizing the document reconstruction error and the error in a classification model learned in the hidden space using known labels from the source domain. The partial observations in the target space are then mapped to the same hidden space, and classified into the label space determined by the source domain. Exemplary results provided for a Twitter dataset demonstrate that the method identifies meaningful hidden topics and provides useful classifications of specific tweets. | 07-19-2012 |
20120185416 | LOAD ESTIMATION IN USER-BASED ENVIRONMENTS - Method, system, and computer program product for load estimation in a user-based environment. The method includes: inputting a set of time-dependent, raw operational indicators of the environment; creating a load function according to the specific needs of the environment; displaying an estimated load; receiving user feedback on the estimated load; and applying a dynamic learning mechanism to generated a user-tuned load function for estimating load on the environment. The dynamic learning mechanism may be an informative mechanism that supports backtracking to solve user-adaptability problems. | 07-19-2012 |
20120185417 | APPARATUS AND METHOD FOR GENERATING ACTIVITY HISTORY - According to one embodiment, a context acquisition unit acquires a context of a user and a date when the context has occurred. A context storage unit stores the context and the date. An activity information storage unit stores activity information of the user and date information to schedule the activity information. A first assignment unit assigns, to a first date corresponding to the date information, the activity information or an activity label extracted from the activity information. A second assignment unit assigns, to a second date to which the activity information or the activity label is not assigned, an activity label by using the context of the second date and an activity label assignment rule previously trained. | 07-19-2012 |
20120185418 | SYSTEM AND METHOD FOR DETECTING ABNORMAL AUDIO EVENTS - Techniques for detecting abnormal audio events in a given environment, including learning the modeling of the environment to be surveilled during which a database is created by extraction of acoustic parameters associated with audio streams picked up over a fixed time period and an unsupervised automatic segmentation of said streams, followed by grouping the segments in classes and a statistical modeling of the segment classes, a usage phase including analysis of an audio stream, with the extraction of the acoustic parameters, automatic segmentation of said analysed stream substantially identical to that used during the learning phase and determining a likelihood of each statistical model contained in the database for each of the segments of the analysed audio stream, resulting in a likelihood value which is compared to a threshold value to determine the presence or absence of audio anomalies in the analysed audio stream. | 07-19-2012 |
20120185419 | DETERMINING A DYNAMIC USER PROFILE INDICATIVE OF A USER BEHAVIOR CONTEXT WITH A MOBILE DEVICE - Methods, apparatuses and articles of manufacture for use in a mobile device to determine whether a dynamic user profile is to transition from a first state to a second state based, at least in part, on one or more sensed indicators. The dynamic user profile may be indicative of one or more current inferable user behavior contexts for a user co-located with the mobile device. The mobile device may transition a dynamic user profile from a first state to a second state, in response to a determination that the dynamic user profile is to transition from the first state to the second state, and operatively affect one or more functions performed, at least in part, by the mobile device based, at least in part, on the transition of the dynamic user profile to the second state. | 07-19-2012 |
20120191630 | Updateable Predictive Analytical Modeling - Methods, systems, and apparatus, including computer programs encoded on one or more computer storage devices, for training and retraining predictive models. A series of training data sets for predictive modeling can be received, e.g., over a network from a client computing system. The training data included in the training data sets is different from initial training data that was used with multiple training functions to train multiple trained predictive models stored in a predictive model repository. The series of training data sets are used with multiple trained updateable predictive models obtained from the predictive model repository and multiple training functions to generate multiple retrained predictive models. An effectiveness score is generated for each of the retrained predictive models. A first trained predictive model is selected from among the trained predictive models included in the predictive model repository and the retrained predictive models based on their respective effectiveness scores. | 07-26-2012 |
20120191631 | Dynamic Predictive Modeling Platform - Methods, systems, and apparatus, including computer programs encoded on one or more computer storage devices, for training and retraining predictive models. A series of training data sets are received and added to a training data queue. In response to a first condition being satisfied, multiple retrained predictive models are generated using the training data queue, multiple updateable trained predictive models obtained from a repository of trained predictive models, and multiple training functions. In response to a second condition being satisfied, multiple new trained predictive models are generated using the training data queue, at least some training data stored in a training data repository and training functions. The new trained predictive models include static trained predictive models and updateable trained predictive models. The repository of trained predictive models is updated with at least some of the retrained predictive models and new trained predictive models. | 07-26-2012 |
20120191632 | SYSTEM AND METHODS FOR FINDING HIDDEN TOPICS OF DOCUMENTS AND PREFERENCE RANKING DOCUMENTS - Systems and methods are disclosed to perform preference learning on a set of documents includes receiving raw input features from the set of documents stored on a data storage device; generating polynomial combinations from the raw input features; generating one or more parameters; applying the parameters to one or more classifiers to generate outputs; determining a loss function and parameter gradients and updating parameters determining one or more sparse regularizing terms and updating the parameters; and expressing that one document is preferred over another in a search query and retrieving one or more documents responsive to the search query. | 07-26-2012 |
20120191633 | System and Method For Failure Prediction For Artificial Lift Systems - A computer-implemented reservoir prediction system, method, and software are provided for failure prediction for artificial lift systems, such as sucker rod pump systems. The method includes a production well associated with an artificial lift system and data indicative of an operational status of the artificial lift system. One or more features are extracted from the artificial lift system data. Data mining is applied to the one or more features to determine whether the artificial lift system is predicted to fail within a given time period. An alert is output indicative of impending artificial lift system failures. | 07-26-2012 |
20120191634 | STORAGE POLICY EVALUATION IN A COMPUTING ENVIRONMENT - Systems and methods for generating a storage policy for a storage system are provided. The method comprises receiving a target function applicable to a storage system having one or more data storage mediums, wherein the target function represents values for storage parameters associated with productivity or loss tolerance in the storage system; implementing one or more simulation rules according to the received target function; generating one or more storage operation requests to access data on said one or more data storage mediums based on said one or more simulation rules; submitting said one or more storage operation requests to the storage system for processing; analyzing simulation results obtained for the storage system, in response to the storage system processing said one or more storage operation requests; and generating one or more storage policies, by a machine learning entity, in response to analyzing the simulation results. | 07-26-2012 |
20120197826 | INFORMATION MATCHING APPARATUS, METHOD OF MATCHING INFORMATION, AND COMPUTER READABLE STORAGE MEDIUM HAVING STORED INFORMATION MATCHING PROGRAM - The information matching apparatus includes: a training data rule setting unit that sets rules defining conditions for a training data of a positive example that is a pair of the records to be judged to be identical and a training data of a negative example that is a pair of the records to be judged to be non-identical; and a training data generating unit that, for the record of a matching source, generates a training data of the positive example by searching for the records of a matching target by using a positive example rule that is a rule defining conditions for the training data of the positive example, and generates a training data of the negative example by searching for the records of the matching target by using a negative example rule that is a rule defining conditions for the training data of the negative example. | 08-02-2012 |
20120197827 | INFORMATION MATCHING APPARATUS, METHOD OF MATCHING INFORMATION, AND COMPUTER READABLE STORAGE MEDIUM HAVING STORED INFORMATION MATCHING PROGRAM - The information matching apparatus includes: a training data setting unit that sets supervised data in a machine learning device of supervised learning that learns judgment criteria used for a judgment of identicalness, similarity, and relevance between a plurality of records by matching the records configured by sets of values corresponding to items; a check point setting unit that sets a check point configured by one set of two records used for evaluating the set supervised data; and a learning result evaluation unit, for the set check point, acquires a change between a judgment result using judgment criteria derived as a result of learning based on set first supervised data and a judgment result using judgment criteria derived as a result of learning based on set second supervised data set and evaluates the supervised data based on the acquired change. | 08-02-2012 |
20120197828 | Energy Saving Control for Data Center - A data center includes at least one rack containing electronic devices, a data center air conditioning system (DCAC), and an environmental parameter monitoring system. At least one set of eligible environmental parameters is determined that satisfies the cooling demand of the at least one rack containing electronic devices. According to the at least one set of eligible environmental parameters and corresponding relationships between sets of setting parameters of the DCAC and corresponding sets of environmental parameters determined by an artificial neural network, plural sets of setting parameters of the DCAC are determined. A power consumption of the DCAC to which each set of setting parameters in the plural sets of setting parameters corresponds is obtained. A set of setting parameters for which the corresponding power consumption satisfies a predetermined condition for energy saving is selected and us to set the DCAC. | 08-02-2012 |
20120203717 | Learning Similarity Function for Rare Queries - Techniques are described for determining queries that are similar to rare queries. An n-gram space is defined to represent queries and a similarity function is defined to measure the similarities between queries. The similarity function is learned by leveraging training data derived from user behavior data and formalized as an optimization problem using a metric learning approach. Furthermore, the similarity function can be defined in the n-gram space, which is equivalent to a cosine similarity in a transformed n-gram space. Locality sensitive hashing can be exploited for efficient retrieval of similar queries from a large query repository. This technique can be used to enhance the accuracy of query similarity calculation for rare queries, facilitate the retrieval of similar queries and significantly improve search relevance. | 08-09-2012 |
20120203718 | ALGORITHM ENGINE FOR USE IN A PATTERN MATCHING ACCELERATOR - A pattern matching accelerator (PMA) for assisting software threads to find the presence and location of strings in an input data stream that match a given pattern. The patterns are defined using regular expressions that are compiled into a data structure comprised of rules subsequently processed by the PMA. The patterns to be searched in the input stream are defined by the user as a set of regular expressions. The patterns to be searched are grouped in pattern context sets. The sets of regular expressions which define the pattern context sets are compiled to generate a rules structure used by the PMA hardware. The rules are compiled before search run time and stored in main memory, in rule cache memory within the PMA or a combination thereof. For each input character, the PMA executes the search and returns the search results. | 08-09-2012 |
20120203719 | AUDIO SIGNAL PROCESSING DEVICE, AUDIO SIGNAL PROCESSING METHOD, AND PROGRAM - An audio signal processing device includes: a time-frequency analysis unit performing a time-frequency analysis of an input audio signal; a base factorization unit inputting learning data that is generated in advance based on an audio signal for learning including a sound from a plurality of sound sources and is made with base frequencies corresponding to the respective sound sources and carrying out base factorization of a time-frequency analysis result to the input audio signal inputted from the time-frequency analysis unit by applying a total base frequency that has the base frequencies corresponding to the respective sound sources combined therein to generate a base activity to the input audio signal; and a command identification unit inputting the base activity from the base factorization unit to carry out command identification by performing an identification process of the inputted base activity. | 08-09-2012 |
20120203720 | ROBUST PATTERN RECOGNITION SYSTEM AND METHOD USING SOCRATIC AGENTS - A computer-implemented pattern recognition method, system and program product, the method comprising in one embodiment: creating electronically a linkage between a plurality of models within a classifier module within a pattern recognition system such that any one of said plurality of models may be selected as an active model in a recognition process; creating electronically a null hypothesis between at least one model of said plurality of linked models and at least a second model among said plurality of linked models; accumulating electronically evidence to accept or reject said null hypothesis until sufficient evidence is accumulated to reject said null hypothesis in favor of one of said plurality of linked models or until a stopping criterion is met; and transmitting at least a portion of the electronically accumulated evidence or a summary thereof to accept or reject said null hypothesis to a pattern classifier module. | 08-09-2012 |
20120209794 | SELF-ORGANIZING SEQUENTIAL MEMORY PATTERN MACHINE AND REINFORCEMENT LEARNING METHOD - A self-organizing computing machine utilizes a method for mapping from a plurality of patterns contained within provided inputs to an invariant perception, distinguishable by a name or a label. The self-organizing computing machine includes a network of at least three nodes arranged in at least two hierarchical levels, at least one feature extractor, and at least one output unit arranged to interface the invariant perception. The nodes may include a reinforcement learning sub-network combined with an ensemble learning sub-network. The reinforcement learning sub-network may be arranged to receive at least two correlants, to determine a plurality of output values and to output the output values to the nodes of the higher level and the nodes of the lower level. Also, the ensemble learning sub-network may be arranged to receive and to combine output values from nodes of the higher level and nodes of the lower level. | 08-16-2012 |
20120209795 | WEB PAGE ANALYSIS SYSTEM FOR COMPUTERIZED DERIVATION OF WEBPAGE AUDIENCE CHARACTERISTICS - A system for generating a demographic profile for a set of at least one webpage, the system comprising a webpage audience information gatherer operative for providing, for at least one webpage, training data including demographic information characterizing an audience of the webpage; a predictor developing system operative to compute at least one content characteristic of said webpage and to develop a prediction process which if applied to said content characteristic would have predicted said training data; and a webpage audience predictor operative, for at least one new webpage, whose audience is unknown, to compute at least one content characteristic of the new webpage and to generate predicted demographic information predicted to characterize said unknown audience of said new webpage by applying said prediction process to said new webpage's content characteristic. | 08-16-2012 |
20120209796 | ATTENTION FOCUSING MODEL FOR NEXTING BASED ON LEARNING AND REASONING - A system and method for nexting is presented. The method comprises computing an expected event, observing a new event, when the expected event matches the new event, processing the new event and performing action in accordance with given concepts, when the expected event does not match the new event and the new event can be explained based on the given concepts, processing the new event and performing action in accordance with the given concepts, and when the expected event does not match the new event and the new event cannot be explained based on the given concepts, employing learning mechanism and performing action decided on by the learning mechanism. In one aspect, the method comprises generating new concepts using reasoning or learning. In one aspect, the method comprises converting sensed numerical data into events of interest via the application of learned functions operating on the numerical data. | 08-16-2012 |
20120209797 | System And Method For Facilitating Evergreen Discovery Of Digital Information - A computer-implemented system and method for facilitating evergreen discovery of digital information is provided. A hierarchy of topics for topically-limited subject areas is defined. Seed words characteristic of each topic are selected. Training material from the digital information that corresponds to the respective subject area of each of the topics is designated. Candidate topic models are formed from the seed words. Each candidate topic model includes a pattern evaluable against the digital information. An ability of each of the candidate topic models to identify such digital information matching the candidate topic model's topic is tested by matching the pattern in the candidate topic model to the training material. The candidate topic model for each topic that includes the highest abilities with respect to the topic in performance, simplicity and bias is chosen. An evergreen index is formed by pairing the chosen candidate topic model to each topic in the hierarchy. | 08-16-2012 |
20120215727 | AUTOMATIC DATA CLEANING FOR MACHINE LEARNING CLASSIFIERS - Systems and techniques for improving the training of machine learning classifiers are disclosed. A classifier is trained using a set of validated documents that are accurately associated with a set of class labels. A subset of non-validated documents is also identified and is used to further train and improve accuracy of the classifier. | 08-23-2012 |
20120221494 | REGULAR EXPRESSION PATTERN MATCHING USING KEYWORD GRAPHS - Expanding a regular expression set into an expanded expression set that recognizes a same language as the regular expression set and includes more expressions than the regular expression set, with less operators per expression includes: logically connecting the expressions in the regular expression set; parsing the expanded expression set; transforming the parsed expanded expression set into a Glushkov automata; transforming the Glushkov automata into a modified deterministic finite automaton in order to maintain fundamental graph properties; combining the modified DFA into a keyword graph using a combining algorithm that preserves the fundamental graph properties; and computing an Aho-Corasick fail function for the keyword graph using a modified algorithm to produce a modified Aho-Corasick graph with a goto and a fail function and added information per state. | 08-30-2012 |
20120221495 | DIGITAL WEIGHT LOSS AID - A health management system provides instantaneous feedback as to the relationship of food items and exercise to one's fitness level, including one's weight. The health management system does not require the user to count calories, either on the intake or expenditure side of the weight loss paradigm. Rather, the health management system may use icons and graphic displays, without units, to provide a user-friendly interface. The health management system can integrate weight, food intake and activity and can learn the individual's unique response to each element to predict the direction of weight gain or loss. | 08-30-2012 |
20120221496 | Text Classification With Confidence Grading - A computer implemented method and system is provided for classifying a document. A classifier is trained using training documents. A list of first words is obtained from the training documents. A prior probability is determined for each class of multiple classes. Conditional probabilities are calculated for the first words for each class. Confidence thresholds are determined. Confidence grades are defined for the classes using the confidence thresholds. A list of second words is obtained from the document. Conditional probabilities for the list of second words are determined from the calculated conditional probabilities for the list of first words. A posterior probability is calculated for each of the classes and compared with the determined confidence thresholds. Each class is assigned to one of the defined confidence grades based on the comparison. The document is assigned to one of the classes based on the posterior probability and the assigned confidence grades. | 08-30-2012 |
20120221497 | Regular Expression Processing Automaton - A method and corresponding apparatus are provided implementing a stage one of run time processing using Deterministic Finite Automata (DFA) and implementing a stage two of run time processing using Non-Deterministic Finite Automata (NFA) to find the existence of a pattern in a payload, such as the payload portion of an Internet Protocol (IP) datagram, or an input stream. | 08-30-2012 |
20120221498 | AGGREGATING AND NORMALIZING ENTERTAINMENT MEDIA - Disclosed are methods for making disparate entertainment media content (e.g., television or movies) from multiple sources available through a single interface of a user device. Content of varying data formats from multiple data sources are aggregated. Classifications of the media data are created which can include assigning content into clusters. The data are normalized, and attributes of the data are curated. Features also are provided to automatically synchronize, obtain, and update media content on the media sources and on client devices. Various ways of handling data aggregation and normalization issues associated with compiling media data also are described. | 08-30-2012 |
20120221499 | WORKLOAD LEARNING IN DATA REPLICATION ENVIRONMENTS - A method for replicating I/O performance in data replication environments, such as PPRC environments, is described. In selected embodiments, such a method includes monitoring I/O workload at a primary storage device over a period of time, such as a period of hours, days, or months. The method then generates learning data at the primary storage device describing the I/O workload over the selected time period. The learning data is replicated from the primary storage device to a secondary storage device. The method uses the learning data to optimize the secondary storage device to handle the I/O workload of the primary storage device. This will enable the secondary storage device to provide substantially the same I/O performance as the primary storage device in the event a failover occurs. | 08-30-2012 |
20120226639 | Systems and Methods for Processing Machine Learning Algorithms in a MapReduce Environment - Systems and methods for processing Machine Learning (ML) algorithms in a MapReduce environment are described. In one embodiment of a method, the method includes receiving a ML algorithm to be executed in the MapReduce environment. The method further includes parsing the ML algorithm into a plurality of statement blocks in a sequence, wherein each statement block comprises a plurality of basic operations (hops). The method also includes automatically determining an execution plan for each statement block, wherein at least one of the execution plans comprises one or more low-level operations (lops). The method further includes implementing the execution plans in the sequence of the plurality of the statement blocks. | 09-06-2012 |
20120226640 | Behavior and information model to yield more accurate probability of successful outcome - A report indicating a user-reported probability of a successful outcome is received. A behavior and information model is estimated based on the report. The behavior and information model includes a behavior model component having a bias parameter and a consistency parameter. The behavior and information model includes an information model component having a first user-believed probability of a successful outcome and a second user-believed probability of a successful outcome. The behavior and information model is used to yield a model-determined probability of a successful outcome that more accurately reflects a probability of a successful outcome than the user-reported probability of a successful outcome does. | 09-06-2012 |
20120226641 | TRAINING A SEARCH QUERY INTENT CLASSIFIER USING WIKI ARTICLE TITLES AND A SEARCH CLICK LOG - Techniques are described herein for training a search query intent classifier using wiki article titles and a search click log. Titles of wiki articles that correspond to links that are associated with a specified wiki article and/or titles of wiki articles that are included in a category that includes the specified wiki article are extracted and included with the title of the specified wiki article in an initial set. Each title in the initial set is correlated with respective clicked URI(s) using a search click log. The initial set is expanded to include search terms that are correlated to the clicked URIs based on the search click log to provide an expanded set. The search query intent classifier is trained to classify search queries with respect to a query intent that is associated with the title of the specified wiki article based on the expanded set. | 09-06-2012 |
20120226642 | METHOD AND APPARATUS FOR CONSIDERING MULTI-USER PREFERENCE BASED ON MULTI-USER-CRITERIA GROUP - A method and apparatus for decision making considering a multi-user preference based on a multi-user-criterion group are provided. The method includes determining user information using ontology, determining an appointed area and an appointed category based on the user information, determining appointed candidate places belonging to the appointed area and appointed category, and determining a final appointed place among the appointed candidate places based on a user preference. | 09-06-2012 |
20120226643 | EMPIRICAL DATA MODELING - Methods, apparatuses and systems directed to pattern identification and pattern recognition. In some particular implementations, the invention provides a flexible pattern recognition platform including pattern recognition engines that can be dynamically adjusted to implement specific pattern recognition configurations for individual pattern recognition applications. In some implementations, the present invention also provides for a partition configuration where knowledge elements can be grouped and pattern recognition operations can be individually configured and arranged to allow for multi-level pattern recognition schemes. | 09-06-2012 |
20120233096 | OPTIMIZING AN INDEX OF WEB DOCUMENTS - Historical usage data related to user queries and training properties for a plurality of web pages is received and utilized to train a mathematical model to predict the likelihood of retrieval of a web page during a web search. Properties are extracted from the plurality of web pages in the index and the mathematical model is applied to the properties for each web page to calculate a sortrank value. The index is reordered based on the sortrank value such that the web pages most likely to be retrieved by a user submitting a search query appear first in the index. After a search query is received from a user the index is traversed in an order determined by the sortrank value. Responsive web pages are presented to the user in an order determined by a search engine ranking algorithm. | 09-13-2012 |
20120233097 | Multiple Hypothesis Tracking - Embodiments described herein are directed to multiple hypothesis systems and methods for tracking observations that are domain agnostic and involves determining the probability that a given set of observations (i.e., a track) corresponds to a particular target, object or linked set of events. One embodiment described herein relates to cyber security tracking methods and systems. | 09-13-2012 |
20120233098 | Multiple Hypothesis Tracking - Embodiments described herein are directed to multiple hypothesis systems and methods for tracking observations that are domain agnostic and involves determining the probability that a given set of observations (i.e., a track) corresponds to a particular target, object or linked set of events. One embodiment described herein relates to cyber security tracking methods and systems. | 09-13-2012 |
20120233099 | OPTIMIZATION PROBLEM SOLVING - One method includes assigning one of a number of predefined values to each of a number of shadow prices of the system, distributing the assigned predefined shadow price values to a number of sub-problems, wherein each sub-problem is associated with one of a number of subsystems of the system, performing an analysis, including: determining a parametric solution and a region of validity for each of the number of sub-problems, determining an intersection of the regions of validity of all the parametric solutions, determining whether the optimization problem is solved from the parametric solutions, determining one or more shadow price updates based on the parametric solutions, and distributing the updated shadow prices to sub-problems having a region of validity that does not include the updated shadow prices, and repeating the analysis using the updated shadow prices until the optimization problem is solved from the parametric solutions of the number of sub-problems. | 09-13-2012 |
20120233100 | Active Learing Decision Engines - Systems and methods for active learning decision engines in accordance with embodiments of the invention are disclosed. In one embodiment of the invention, an active learning decision engine includes equivalence class storage, hypotheses storage, edge storage, test storage, where tests are related to hypotheses, observation storage; and a processor, where the processor is configured to determine a plurality of equivalence classes containing one or more hypotheses, determine a set of edges utilizing tests, where the edges in the set of edges span hypotheses in distinct equivalence classes, determine weights for the determined edges, select a test based on the determined weights, perform the selected test and observe the results of the performed test, remove edges from the set of edges utilizing the observed results, and select a hypothesis from the one or more hypotheses using the set of edges. | 09-13-2012 |
20120233101 | CLUSTER ANALYSIS SYSTEM AND METHOD TO IMPROVE SORTING PERFORMANCE - A method for classifying an unknown part includes acquiring a broadband frequency response for a plurality of parts in a training set of parts, the training set of parts including a plurality of non-flawed parts and a plurality of flawed parts, performing a statistical analysis on the broadband frequency responses to form a plurality of part subsets, the plurality of part subsets including at least one subset of non-flawed parts and at least one subset of flawed parts, and utilizing the plurality of part subsets to form a blended subset of parts, the blended subset of parts being used to classify an unknown part as either a defective part or a non-defective part. A tool for implementing the method is also described. | 09-13-2012 |
20120239598 | Machine Learning Method to Identify Independent Tasks for Parallel Layout in Web Browsers - Methods and devices for accelerating web page rendering include processing web pages and gathering web page element information, performing machine learning analysis on the gathered web page element information to identify patterns in layout independence correlated to web page element information, and training a classifier to predict sub-tree independence based on element information in a web page script. The predicted sub-tree independence may be used to concurrently process portions of a web page to be rendered to reduce the time required to render the page. Sub-trees may be conditionally independent, in which case, the conditionally independent sub-trees may be made independent by speculating data to render the sub-trees independent, or by performing a task to obtain the certain information to render the sub-tree independent. | 09-20-2012 |
20120239599 | COMPUTER PRODUCT, DATA ANALYZING METHOD, AND DATA ANALYZING APPARATUS - A computer-readable medium stores a program that causes a computer, which has a memory device storing a set of measured values that include a set of positive case measured values for which an objective variable for an explanatory variable group of one or more explanatory variables represents a positive case and a set of negative case measured values for which the objective variable for the explanatory variable group represents a negative case, to execute a process. The process includes extracting randomly, a positive case measured value group and a negative case measured value group from the set of measured values such that the positive case measured values and the negative case measured values extracted are equivalent in number; and generating based on the positive case measured value group and the negative case measured value, a prediction equation that predicts the objective variable for a prediction algorithm. | 09-20-2012 |
20120239600 | METHOD FOR TRAINING AND USING A CLASSIFICATION MODEL WITH ASSOCIATION RULE MODELS - A classification model is trained and used for detecting patterns in input data. The training of the model includes retrieving a set of previously recorded input data containing a plurality of items associated with a plurality of entities and adding to each entity a known classification. Furthermore, training the model includes determining rules from the set of previously recorded input data and the known classification by associating the classification of each entity with the respective items of said entity. The training of the model further includes determining a set of rules which are applicable, aggregating the lift values of the rules determined for said entity, and predicting a classification based on the aggregated association values for each entity. The resulting aggregated lift value together with the respective entity and classification are used as input for a standard classification algorithm, where the result is a classification model. | 09-20-2012 |
20120246097 | Apparatus and Methods for Analyzing and Using Short Messages from Commercial Accounts - Disclosed are methods and apparatus for analyzing and using online short messages from promoting entity accounts (e.g., business or non-profit accounts). In one embodiment, a method of analyzing and using messages sent for a plurality of promoting entity accounts is disclosed. A plurality of models for classifying a plurality of messages based on a plurality of message features are obtained for each message. Each message is sent via a computer network between a selected one of the promoting entity accounts and one or more subscribing users that subscribe to receive messages from such selected promoting entity account, and each model is trained to identify whether a message belongs to a particular class based on a lexicon that was generated for such particular class and a training set of messages that belong to the particular class and message that do not belong to the particular class. A new message is classified based on the models and retaining classification information regarding the new message in a database that is accessible by a user so as to review the classification information on a computer display. | 09-27-2012 |
20120246098 | Role Mining With User Attribution Using Generative Models - Applications of machine learning techniques such as Latent Dirichlet Allocation (LDA) and author-topic models (ATM) to the problems of mining of user roles to specify access control policies from entitlement as well as logs which contain record of the usage of these entitlements are provided. In one aspect, a method for performing role mining given a plurality of users and a plurality of permissions is provided. The method includes the following steps. At least one generative machine learning technique, e.g., LDA, is used to obtain a probability distribution θ for user-to-role assignments and a probability distribution β for role-to-permission assignments. The probability distribution θ for user-to-role assignments and the probability distribution β for role-to-permission assignments are used to produce a final set of roles, including user-to-role assignments and role-to-permission assignments. | 09-27-2012 |
20120246099 | LEARNING DEVICE, LEARNING METHOD, AND COMPUTER PROGRAM PRODUCT - According to an embodiment, a learning device includes a selecting unit, a learning unit, and an evaluating unit. The selecting unit performs a plurality of selection processes of selecting a plurality of groups including one or more learning samples from a learning sample storage unit, where respective learning samples are classified into any one of a plurality of categories. The learning unit learns a classification metric and obtains a set of a classification metric. The evaluating unit acquires two or more evaluation samples of different categories from an evaluation sample storage unit where respective evaluation samples are classified into any one of a plurality of categories; evaluates the classification metric included in the set of the classification metric using the two or more acquired evaluation samples; acquires a plurality of classification metric corresponding to the evaluation results from the set of the classification metric; and thereby generates an evaluation metric including the plurality of classification metric. | 09-27-2012 |
20120246100 | METHODS AND SYSTEMS FOR EXTRACTING KEYPHRASES FROM NATURAL TEXT FOR SEARCH ENGINE INDEXING - The present invention is a method and system for the extraction of keyphrases from natural text. For the purpose of this document, keyphrases are text segments that represent the main topic of a text. The method of the present invention may facilitate keyphrase extraction from any length of text. The text may be of several varieties, such as, for example a sentence, paragraph, document or collection of documents. Phrase separator methods may be applied to the text to extract phrases from the text. From these phrases the present invention may identify the one or more phrases that are integral to the meaning of the text and these may be identified as the keyphrases of the text. The text may be indexed using the keyphrases so that a search based upon any of the keyphrases will cause search engines and/or text retrieval means to retrieve the text. | 09-27-2012 |
20120254076 | SUPERVISED RE-RANKING FOR VISUAL SEARCH - Supervised re-ranking for visual search may include re-ordering images that are returned in response to a text-based image search by exploiting visual information included in the images. In one example, supervised re-ranking for visual search may include receiving a textual query, obtaining an initial ranking result including a plurality of images corresponding to the textual query, and representing the textual query by a visual context of the plurality of images. A query-independent re-ranking model may be trained based on visual re-ranking features of the plurality of images of the textual query in accordance with a supervised training algorithm. | 10-04-2012 |
20120254077 | Data Driven Frequency Mapping for Kernels Used in Support Vector Machines - Frequency features to be used for binary classification of data using a linear classifier are selected by determining a set of hypotheses in a d-dimensional space using d-dimensional labeled training data. A mapping function is constructed for each hypothesis. The mapping functions are applied to the training data to generate frequency features, and a subset of the frequency are selecting iteratively. The linear function is then trained using the subset of frequency features and labels of the training data. | 10-04-2012 |
20120254078 | MARKOV MODELING OF SERVICE USAGE PATTERNS - A system for analyzing service usage utilizing Markov models. Records of client requests to the service are extracted from at least one log. The records are grouped by client and sorted by timestamp. A pattern of requests that form an action is detected. Each action has a time. A probability is calculated of a transition from a precedent action to a subsequent action, where the precedent action has a time prior to the subsequent action. A delay time is also calculated between a precedent action and a subsequent action. A probability is calculated for a delay time, such as the likelihood that a delay from a precedent action to a subsequent action will fall within a given time interval. | 10-04-2012 |
20120254079 | Serendipitous Recommendations System and Method - A computer-implemented serendipitous recommendations system and method generates recommendations for delivery to system users in accordance with settings of desired levels of serendipity, including serendipity levels established through use of serendipity tuning controls operable by users. The recommendations are informed by an interest affinity anomaly function that identifies contrasting interest affinities between recommendation recipients and other users. Explanations may be generated that provide reasons as to why a recommendation was delivered to a user, and the explanation may include a selection of phrases that are influenced by a serendipity level setting, and may include an expression of a level of confidence with regard to the recommendation. | 10-04-2012 |
20120254080 | Markov Modeling of Service Usage Patterns - A system for analyzing service usage utilizing Markov models. Records of client requests to the service are extracted from at least one log. The records are grouped by client and sorted by timestamp. A pattern of requests that form an action is detected. Each action has a time. A probability is calculated of a transition from a precedent action to a subsequent action, where the precedent action has a time prior to the subsequent action. A delay time is also calculated between a precedent action and a subsequent action. A probability is calculated for a delay time, such as the likelihood that a delay from a precedent action to a subsequent action will fall within a given time interval. | 10-04-2012 |
20120254081 | OPTIMIZATION CONTROL SYSTEM - Provided is an optimization control system in an attempt to improve searching accuracy of an optimal solution defining a behavior mode for a control subject. A plan storing element | 10-04-2012 |
20120259801 | TRANSFER OF LEARNING FOR QUERY CLASSIFICATION - Transfer of learning trains a new domain for the classification of search queries according to different tasks, as well as the generation of a corresponding domain-specific query classifier that may be used to classify the search queries according to the different tasks in the new domain. The transfer of learning may include preparing a new domain to receive classification knowledge from one or more source domains by populating the new domain with preliminary query patterns extracted for a search engine log. The transfer of learning may further include preparing the classification knowledge in each source domain for transfer to the new domain. The classification knowledge in each source domain may then be transferred to the new domain. | 10-11-2012 |
20120259802 | ACTIVE LEARNING OF RECORD MATCHING PACKAGES - An active learning record matching system and method for producing a record matching package that is used to identify pairs of duplicate records. Embodiments of the system and method allow a precision threshold to be specified and then generate a learned record matching package having precision greater than this threshold and a recall close to the best possible recall. Embodiments of the system and method use a blocking technique to restrict the space of record matching packages considered and scale to large inputs. The learning method considers several record matching packages, estimates the precision and recall of the packages, and identifies the package with maximum recall having precision greater than equal to the given precision threshold. A human domain expert labels a sample of record pairs in the output of the package as matches or non-matches and this labeling is used to estimate the precision of the package. | 10-11-2012 |
20120265716 | MACHINE LEARNING OF KNOWN OR UNKNOWN MOTION STATES WITH SENSOR FUSION - Example methods, apparatuses, or articles of manufacture are disclosed herein that may be utilized, in whole or in part, to facilitate or support one or more operations or techniques for machine learning of known or unknown motion states with sensor fusion. | 10-18-2012 |
20120265717 | LEARNING SITUATIONS VIA PATTERN MATCHING - Example methods, apparatuses, or articles of manufacture are disclosed herein that may be utilized, in whole or in part, to facilitate or support one or more operations or techniques for machine learning of situations via pattern matching or recognition. | 10-18-2012 |
20120265718 | METHOD AND APPARATUS FOR EVOLVING A QUANTUM SYSTEM USING A MIXED INITIAL HAMILTONIAN COMPRISING BOTH DIAGONAL AND OFF-DIAGONAL TERMS - Various adaptations to adiabatic quantum computation and quantum annealing are described. These adaptations generally involve tailoring an initial Hamiltonian so that a local minimum is avoided when a quantum processor is evolved from the initial Hamiltonian to a problem Hamiltonian. The initial Hamiltonian may represent a mixed Hamiltonian that includes both diagonal and off-diagonal terms, where the diagonal terms at least partially define a center point of a first computation space that is at least partially contained within a second computation space. A problem Hamiltonian may be evolved into a low energy state by inhomogeneously inducing disorder in the qubits of the quantum processor. A higher degree of disorder may be induced in a subset of qubits predicted to contribute to a local minimum of the problem Hamiltonian. | 10-18-2012 |
20120271782 | Method and apparatus for event detection permitting per event adjustment of false alarm rate - Method and apparatus for object or event of interest detection which minimizes the level of false alarms and maximizes the level of detections as defined on a per event or object basis by the analyst. The invention allows for the minimization of false alarms for objects or events of interest which have a close resemblance to all other objects or events mapped to the same multidimensional feature space, and allows for the per event or per object adjustment on false alarms for objects or events of higher interest. | 10-25-2012 |
20120278261 | DETERMINING THE IMPORTANCE OF DATA ITEMS AND THEIR CHARACTERISTICS USING CENTRALITY MEASURES - Computer-implemented methods, systems, and articles of manufacture for determining the importance of a data item. A method includes: (a) receiving a node graph; (b) approximating a number of neighbor nodes of a node; and (c) calculating a average shortest path length of the node to the remaining nodes using the approximation step, where this calculation demonstrates the importance of a data item represented by the node. Another method includes: (a) receiving a node graph; (b) building a decomposed line graph of the node graph; (c) calculating stationary probabilities of incident edges of a node graph node in the decomposed line graph, and (d) calculating a summation of the stationary probabilities of the incident edges associated with the node, where the summation demonstrates the importance of a data item represented by the node. Both methods have at least one step carried out using a computer device. | 11-01-2012 |
20120278262 | Suggesting Users for Interacting in Online Applications in a Social Networking Environment - Users of a social networking system are matched with other users of the social networking system based on the likelihood of both users' being interested in using a social application and the likelihood that they would want to interact with each other using the application. Social applications include social games and other applications associated with a social networking system in which users can interact with other users. The social networking system selects for a particular user other candidate users. This selection may be based on at least one of a predicted likelihood that a user would invite the candidate users to use the social application and/or a predicted likelihood that the selected candidate user would accept the user's invitation. The user may choose to act on the suggestion by inviting the other user and the invited user may accept, reject, or ignore the invitation. | 11-01-2012 |
20120278263 | COST-SENSITIVE ALTERNATING DECISION TREES FOR RECORD LINKAGE - Record Linkage (RL) is the task of identifying two or more records referring to the same entity (e.g., a person, a company, etc.). RL models can be based on Cost Sensitive Alternating Decision Trees (ADTree), an algorithm that uniquely combines boosting and decision trees algorithms to create shorter and easier-to-interpret linking rules. These models can be naturally trained to operate at industrial precision/recall operating points, and the shorter output rules are so clear that it can effectively explain its decisions to non-technical users via score aggregation or visualization. The models significantly outperform other baselines on the desired industrial operating points, and the improved understanding of the model's decisions led to faster debugging and feature development cycles. | 11-01-2012 |
20120278264 | TECHNIQUES TO FILTER MEDIA CONTENT BASED ON ENTITY REPUTATION - Techniques to filter media content based on entity reputation are described. An apparatus may comprise a reputation subsystem that manages an entity reputation score for an entity. The reputation subsystem comprising a reputation manager component and a reputation input/output (I/O) component. The reputation manager component may include a data collection module that collects reputation information for an entity from a selected set of multiple reputation sources. The reputation manager component may also comprise a feature manager module, communicatively coupled to the data collection module, that extracts a selected set of reputation features from the reputation information. The reputation manager component may further comprise a reputation scoring module, communicatively coupled to the feature manager module, that generates an entity reputation score based on the reputation features using a supervised or unsupervised machine learning algorithm. Other embodiments are described and claimed. | 11-01-2012 |
20120284212 | Predictive Analytical Modeling Accuracy Assessment - A system includes a computer(s) coupled to a data storage device(s) that stores a training function repository and a predictive model repository that includes includes updateable trained predictive models each associated with an accuracy score. A series of training data sets are received, being training samples each having output data that corresponds to input data. The training data is different from initial training data that was used with training functions from the repository to train the predictive models initially. Upon receiving a first training data set included in the series and for each predictive model in the repository, the input data in the first training set is used to generate predictive output data that is compared to the output data. Based on the comparison and previous comparisons determined from the initial training data and from previously received training data sets, an updated accuracy score for each predictive model is determined. | 11-08-2012 |
20120284213 | Predictive Analytical Modeling Data Selection - A system includes a computer(s) coupled to a data storage device(s) that stores a training data repository and a predictive model repository. The training data repository includes retained data samples from initial training data and from previously received data sets. The predictive model repository includes at least one updateable trained predictive model that was trained with the initial training data and retrained with the previously received data sets. A new data set is received. A richness score is assigned to each of the data samples in the set and to the retained data samples that indicates how information rich a data sample is for determining accuracy of the trained predictive model. A set of test data is selected based on ranking by richness score the retained data samples and the new data set. The trained predictive model is accuracy tested using the test data and an accuracy score determined. | 11-08-2012 |
20120284214 | SOFTWARE, DISPLAY AND COMPUTER SYSTEM FOR RUNNING AND PRESENTING IMAGES AS PART OF THERAPY FOR ENHANCING NUMERICAL COGNITION - A method of presenting training materials for training users with developmental dyscalculia or related learning difficulties includes determining a number or a numerical expression to present as part of training the user in developing internal maps to assist with overcoming a learning difficulty, whereby the user can increase a tendency to establish an internal neurological representation of numbers and numerical expression, wherein a numerical expression is a sequence of at least one number and at least one mathematical operator, generating a representation in a virtual space of an arrangement of numbers, including a number line and a representation of the number or the numerical expression, taking into account a resolution of the computer-controlled display that is to be used, and presenting to the user, using the computer-controlled display, a view of the virtual space showing the number line and the representation of the number or the numerical expression. | 11-08-2012 |
20120284215 | METRIC LEARNING APPARATUS - A metric learning apparatus memorizes a learning pattern in a feature space and a category which the learning pattern belongs to, performs variable transformation of the learning pattern to a metric space by a transformation matrix, calculates a transformation matrix having a minimum loss value of a loss function in which the loss value is increased when there is a learning pattern belonging to a different category but closer than learning patterns up to k | 11-08-2012 |
20120284216 | Knowledge-Based Models for Data Centers - Techniques for data center analysis are provided. In one aspect, a method for modeling thermal distributions in a data center includes the following steps. Vertical temperature distribution data is obtained for a plurality of locations throughout the data center and is plotted as an s-curve, wherein the vertical temperature distribution data reflects physical conditions at each of the locations which is reflected in a shape of the s-curve. Each of the s-curves is represented with a set of parameters that characterize the shape of the s-curve, wherein the s-curve representations make up a knowledge base model of predefined s-curve types from which thermal distributions and associated physical conditions at the plurality of locations throughout the data center can be analyzed. The set of parameters that characterize the shape of the s-curve are associated with the physical conditions at the plurality of locations throughout the data center using a machine-learning model. | 11-08-2012 |
20120290510 | MULTI-TASK MACHINE LEARNING USING FEATURES BAGGING AND LOCAL RELATEDNESS IN THE INSTANCE SPACE - A multi-task machine learning component learns a set of tasks comprising two or more different tasks based on a set of examples. The examples are represented by features of a set of features. The multi-task machine learning component comprises a digital processing device configured to learn an ensemble of base rules wherein each base rule is learned for a sub-set of the set of features and comprises a multi-task decision tree (MT-DT) having nodes comprising decision rules for tasks of the set of tasks. An inference component comprises a digital processing device configured to predict a result for at least one task of the set of tasks for an input item represented by features of the set of features using the learned ensemble of base rules. | 11-15-2012 |
20120290511 | Database of affective response and attention levels - A data structure stored in memory including: token instances representing stimuli that influence a user's affective state; the token instances are spread over a long period of time that spans different situations, and a plurality of the token instances have overlapping instantiation periods; data representing levels of user attention in some of the token instances used by an application program to improve the accuracy of a machine learning based affective response model for the user; annotations representing emotional states of the user; the annotations are spread over a long period of time that spans different situations; and linkage information between the token instances, the data representing levels of user attention, and the annotations. | 11-15-2012 |
20120290512 | Methods for creating a situation dependent library of affective response - Generating a situation-dependent library comprising a user's expected response to tokens representing stimuli that influence the user's affective state, including: receiving samples comprising temporal windows of token instances to which the user was exposed, wherein the token instances have overlapping instantiation periods and are spread over a long period of time that spans different situations; wherein at least one token is expected to elicit from the user a noticeably different affective response in the different situations; receiving target values corresponding to the temporal windows of token instances; the target values represent the user's responses to the token instances from the temporal windows of token instances; training a machine learning-based user response model using the samples and the corresponding target values; and analyzing the machine learning-based user response model to generate the situation-dependent library comprising the user's expected response to tokens, which accounts for the variations in the user's affective response in the different situations. | 11-15-2012 |
20120290513 | Habituation-compensated library of affective response - Generating a habituation-compensated library comprising a user's expected response to tokens representing stimuli that influence the user's affective state, the method comprising: receiving samples comprising temporal windows of token instances to which the user was exposed, wherein the token instances have overlapping instantiation periods; the samples further comprise data on previous instantiations of at least one of the token instances from the temporal windows; receiving target values corresponding to the temporal windows of token instances; the target values represent the user's response to the token instances from the temporal windows of token instances; training a machine learning-based user response model using the samples, the data on previous instantiations, and the corresponding target values; and analyzing the machine learning-based user response model to generate the habituation-compensated library, which accounts for the influence of the user's previous exposure to tokens | 11-15-2012 |
20120290514 | Methods for predicting affective response from stimuli - Creating a machine learning-based affective response predictor to predict a user's emotional state after being exposed to tokens representing stimuli that influence the user's affective state, comprising: receiving samples comprising temporal windows of token instances to which the user was exposed; the token instances are spread over a long period of time, and a subset of the token instances originate from same source and have overlapping instantiation periods; receiving target values, which represent affective response annotations of the user and correspond to the temporal windows of token instances; and creating the machine learning-based affective response predictor for the user, which compensates for non-linear effects resulting from the user being exposed to the subset of token instances originating from the same source and having overlapping instantiation periods, by running a machine learning training procedure on input data comprising the samples and the corresponding target values. | 11-15-2012 |
20120290515 | Affective response predictor trained on partial data - Creating a machine learning-based affective response predictor of a user when there are significantly more samples than target values available for training, comprising: receiving samples comprising temporal windows of token instances to which the user was exposed; the token instances are spread over a long period of time; receiving intermittent target values corresponding to a subset of the temporal windows of token instances; the target values represent affective response annotations of the user; creating the machine learning-based affective response predictor of the user, by running a semi-supervised machine learning training procedure on the samples and the intermittent corresponding target values; wherein the machine learning-based affective response predictor is more accurate than a predictor created when training only on the samples that have corresponding target values, since it is capable of learning additional information from the samples comprising temporal windows of token instances without corresponding target values. | 11-15-2012 |
20120290516 | Habituation-compensated predictor of affective response - Creating a machine learning-based habituation-compensated predictor of a user's response to token instances representing stimuli that influence the user's affective state, comprising: receiving samples comprising temporal windows of token instances to which the user was exposed, wherein the token instances have overlapping instantiation periods; the samples further comprise data on previous instantiations of at least one of the token instances from the temporal windows; receiving target values corresponding to the temporal windows of token instances; the target values represent the user's responses to the token instances from the temporal windows of token instances; training the machine learning-based habituation-compensated predictor to predict the user's response to token instances, while accounting for the influence of the user's previous exposure to tokens; wherein the training uses the samples, the data on previous instantiations, and the corresponding target values | 11-15-2012 |
20120290517 | Predictor of affective response baseline values - Calculating a situation-dependent baseline value for a user response to token instances representing stimuli that influence the user's affective state, utilizing large time windows and rapid adjustments to changing situations, including: accessing a database storing annotations representing the user's response to token instances originating from multiple distinct token sources; calculating a first situation-dependent baseline value by weighting annotations retrieved from the database and associated with a first situation identifier, which are spread over a long period of time ‘T’; calculating a second situation-dependent baseline value by weighting annotations retrieved from the database and associated with a second situation identifier; wherein the difference between the first and second situation-dependent baseline values is significant, and the method rapidly adjusts to the situation change by exhibiting an extremely shorter transient time between the first and the second situation-dependent baselines than T/2 | 11-15-2012 |
20120290518 | INTEGRATED SEARCH AND ADAPTIVE DISCOVERY SYSTEM AND METHOD - An integrated search and adaptive discovery system and method integrates contents-based indexing and behavioral-based indexing of collections of computer-implemented objects to generate contextualized and/or personalized recommendations. The degree to which contextualization and/or personalization criteria are applied in generating recommendations can be tuned by the recommendation recipient. Personalization functions are applied that are informed by inferred interests and/or expertise, and personalization vectors can be transformed into a format executable by a search engine. Explanations may be provided to recommendation recipients as to why they received recommendations. | 11-15-2012 |
20120296856 | Recognition Techniques to Enhance Automation In a Computing Environment - Systems and methods for detecting end of a transaction in a computing environment are provided. The method comprises determining a target area in a graphical user environment displayed on a display screen, wherein a change is expected to occur when end of a transaction is reached; masking the target area at least partially to remove content included in the target area that is present before or after the transaction was initiated; monitoring the target area for change in content; and detecting the end of the transaction when the content of the target area has changed. | 11-22-2012 |
20120303555 | Scalable Automatic Data Repair - A computer implemented method for generating a set of updates for a database comprising multiple records including erroneous, missing and inconsistent values, the method comprising using a set of partitioning functions for subdividing the records of the database into multiple subsets of records, allocating respective ones of the records to at least one subset according to a predetermined criteria for mapping records to subsets, applying multiple machine learning models to each of the subsets to determine respective candidate replacement values representing a tuple repair for a record including a probability of candidate and current values for the record, computing probabilities to select replacement values for the record from among the candidate replacement values which maximise the probability for values of the record for an updated database. | 11-29-2012 |
20120303556 | COMPARISON OF MODELING AND INFERENCE METHODS AT MULTIPLE SPATIAL RESOLUTIONS - Embodiments provide a position service experimentation system to enable comparison of modeling and inference methods as well as characterization of input datasets for correspondence to output analytics. Crowd-sourced positioned observations are divided into a training dataset and a test dataset. A beacons model is generated based on the training dataset, while device position estimations are calculated for the test dataset based on the beacons model. The device position estimations are compared to the known position of the computing devices generating the positioned observations to produce accuracy values. The accuracy values are assigned to particular geographic areas based on the position of the observing computing device and aggregated to enable a systematic analysis of the accuracy values based on geographic area and/or positioned observations characteristics. | 11-29-2012 |
20120303557 | INTERACTIVE FRAMEWORK FOR NAME DISAMBIGUATION - A “Name Disambiguator” provides various techniques for implementing an interactive framework for resolving or disambiguating entity names (associated with objects such as publications) for entity searches where two or more same or similar names may refer to different entities. More specifically, the Name Disambiguator uses a combination of user input and automatic models to address the disambiguation problem. In various embodiments, the Name Disambiguator uses a two part process, including: 1) a global SVM trained from large sets of documents or objects in a simulated interactive mode, and 2) further personalization of local SVM models (associated with individual names or groups of names such as, for example, a group of coauthors) derived from the global SVM model. The result of this process is that large sets of documents or objects are rapidly and accurately condensed or clustered into ordered sets by that are organized by entity names. | 11-29-2012 |
20120303558 | SYSTEMS AND METHODS FOR GENERATING MACHINE LEARNING-BASED CLASSIFIERS FOR DETECTING SPECIFIC CATEGORIES OF SENSITIVE INFORMATION - A computer-implemented method may include (1) identifying a plurality of specific categories of sensitive information to be protected by a DLP system, (2) obtaining a training data set for each specific category of sensitive information that includes a plurality of positive and a plurality of negative examples of the specific category of sensitive information, (3) using machine learning to train, based on an analysis of the training data sets, at least one machine learning-based classifier that is capable of detecting items of data that contain one or more of the plurality of specific categories of sensitive information, and then (4) deploying the machine learning-based classifier within the DLP system to enable the DLP system to detect and protect items of data that contain one or more of the plurality of specific categories of sensitive information in accordance with at least one DLP policy of the DLP system. | 11-29-2012 |
20120303559 | CREATION, USE AND TRAINING OF COMPUTER-BASED DISCOVERY AVATARS - In embodiments of the present invention improved capabilities are described for developing, training, validating and deploying discovery avatars embodying mathematical models that may be used for document and data discovery and deployed within large data repositories. | 11-29-2012 |
20120310863 | GENE-SPECIFIC PREDICTION - A gene-specific prediction tool for classifying and interpreting gene tests is described. The prediction tool includes a classifier trained and tested using databases of gene variants and their known phenotypes. The classifier uses differences between features of amino acids in obtaining attributes used to perform classification and generate predictions, including for benign and pathologic outcomes, for uncertain gene variants. | 12-06-2012 |
20120310864 | Adaptive Batch Mode Active Learning for Evolving a Classifier - This disclosure includes various embodiments of apparatuses, systems, and methods for adaptive batch mode active learning for evolving a classifier. A corpus of unlabeled data elements to be classified is received, a batch size is determined based on a score function, a batch of unlabeled data elements having the determined batch size is selected from the corpus and labeled using a labeling agent or oracle, a classifier is retrained with the labeled data elements, these steps are repeated until a stop criterion has been met, for example, the classifier obtains a desired performance on unlabeled data elements in the corpus. The batch size determination and selection of a batch unlabeled data elements may be based on a single score function. The data elements may be video, image, audio, web text, and/or other data elements. | 12-06-2012 |
20120310865 | METHOD FOR GENERATING A MACHINE HEARTBEAT - A method and system for generating a heartbeat of a process including at least one machine configured to perform a process cycle consisting of a plurality of timed events performed in a process sequence under an identified condition includes determining the duration of each of the timed events during the process cycle performed under the identified condition, ordering the durations of the plurality of timed events in the process sequence, and generating a heartbeat defined by the ordered durations of a process cycle. The identified condition may be one of a design intent, baseline, learnt, known, current or prior condition. The variance of the heartbeat between a first and at least a second identified condition may be analyzed to monitor and/or control the process or machine. The system may display the process heartbeat information and may generate a message in response to the heartbeat and/or variance thereof. | 12-06-2012 |
20120310866 | DATA PROCESSING DEVICE, COMPUTER PROGRAM THEREFOR AND DATA PROCESSING METHOD - A plurality of pruning measures (PM) are calculated from a feature amount (CV) of test data (TD) which is input, a plurality of isopycnic surfaces (EC) are plotted and set on a threshold space (SS), a threshold curved surface (SC) in which a decrease in at least one of a plurality of pruning measures (PM) causes an increase in at least one thereof is generated using a portion of one isopycnic surface (EC) as a part, a hypothesis curved surface (HC) of subject data (CD) is generated on the threshold space (SS) to set a position intersecting the threshold curved surface (SC) to a pruning threshold (PS), and a plurality of hypotheses of the subject data (CD) are pruned. Thereby, there is provided a data processing device of which at least one of the recognition speed and the recognition accuracy is higher than in the related art. | 12-06-2012 |
20120310867 | Method for Learning Remote Control and Learning Remote Control Thereof - A learning method and a learning remote control in the present invention include: selecting keys to be taught on the learning remote control, and constructing a key series; performing such operation to each key to be taught as: prompting to the keys to be taught, and then receiving and learning commands from a teaching remote control according to the key to be taught; and when being detected that a key is further selected during the operation to each key to be taught, renewing the key series according to this key, and reexecute the operation to each key. The user follows flexible and simple operating steps during the learning procedure of the learning remote control. The operation is brief, and the learning procedure is simple. The intelligent performance of the learning remote control is improved. | 12-06-2012 |
20120310868 | METHOD AND SYSTEM FOR EXTRACTING AND MANAGING INFORMATION CONTAINED IN ELECTRONIC DOCUMENTS - A method and system that utilize metadata to facilitate extraction and enable management of information contained in electronic documents. Metadata describe content of documents based on composition of their structure and ways information is arranged in a structure. The system makes it possible to automatically manage models used for extraction, and metadata also define a logical schema for managing information extracted. The method includes a preparation step in which metadata and document samples are collected and stored, followed by a training step in which the system utilizes metadata and respective document samples to build and train models used for extraction. Finally, in an extraction step, the system receives a collection of documents and utilizes trained models to extract information that can be stored according to logical schema defined from metadata and can be immediately managed. The system enables methods to be applied to information dispersed throughout large documents. In one preferred embodiment, metadata is supplied by an XSD (XML Schema Definition) and document samples are labeled in a XML format that can be validated by the XSD. | 12-06-2012 |
20120310869 | ACTIVE LEARNING SYSTEMS AND METHODS FOR RAPID PORTING OF MACHINE TRANSLATION SYSTEMS TO NEW LANGUAGE PAIRS OR NEW DOMAINS - Systems and methods for active learning of statistical machine translation systems through dynamic creation and updating of parallel corpus. The systems and methods provided create accurate parallel corpus entries from a test set of sentences, words, phrases, etc. by calculating confidence scores for particular translations. Translations with high confidence scores are added directly to the corpus and the translations with low confidence scores are presented to human translations for corrections. | 12-06-2012 |
20120317059 | SYSTEM AND METHOD FOR SPACE AND RESOURCE OPTIMIZATION - Methods and systems for space and resource optimization are disclosed, including a method comprising receiving a plurality of inputs, transforming the plurality of inputs into at least one or more of (but not limited to) an algorithmic graph and a structural graph based on a domain-specific area using a computer processor. The method further includes creating and applying heuristics for parallelization, performing an optimization run, and analyzing an optimal result produced by the optimization run. | 12-13-2012 |
20120323825 | SYSTEM AND METHODS FOR FINDING HIDDEN TOPICS OF DOCUMENTS AND PREFERENCE RANKING DOCUMENTS - Systems and methods are disclosed to perform preference learning on a set of documents includes receiving raw input features from the set of documents stored on a data storage device; generating polynomial combinations from the raw input features; generating one or more parameters; applying the parameters to one or more classifiers to generate outputs; determining a loss function and parameter gradients and updating parameters determining one or more sparse regularizing terms and updating the parameters; and expressing that one document is preferred over another in a search query and retrieving one or more documents responsive to the search query. | 12-20-2012 |
20120323826 | System and Method for Predicting Political Instability using Bayesian Networks - Disclosed is a system and method for predicting political instability. This instability is predicted for specific countries or geographic regions. In one embodiment, the prediction is carried out on a basis of a probabilistic model, such as a Bayesian-network. The model is comprised of various notes corresponding to dependent and independent variables. The independent variables, in turn, correspond to factors relating to historical political instability. The dependent variable corresponds to the prediction of instability. By populating the independent variables with current data, future political instability can be predicted. | 12-20-2012 |
20120323827 | Generating Predictions From A Probabilistic Process Model - A method for predictive analytics in a semi-structured process including updating, iteratively, at least one probability of a probabilistic process model based on a completed task, wherein updating the at least one probability of the probabilistic process model includes receiving the probabilistic process model associated with a todo list including a plurality of tasks of the semi-structured process, defining a cost of each of the plurality of tasks, prioritizing the plurality of tasks according to the costs, and recommending a next task from the todo list according to a prioritization | 12-20-2012 |
20120323828 | FUNCTIONALITY FOR PERSONALIZING SEARCH RESULTS - A query processing system is described herein for personalizing results for a particular user. The query processing system operates by receiving a query from a particular user u who intends to find results that satisfy the query with respect to a topic T | 12-20-2012 |
20120323829 | GRAPH-BASED CLASSIFICATION BASED ON FILE RELATIONSHIPS - A reliable automated malware classification approach with substantially low false positive rates is provided. Graph-based local and/or global file relationships are used to improve malware classification along with a feature selection algorithm. File relationships such as containing, creating, copying, downloading, modifying, etc. are used to assign malware probabilities and simultaneously reduce the false positive and false negative rates on executable files. | 12-20-2012 |
20120323830 | METHOD FOR AUTOMATICALLY TEACHING PARAMETERS - The invention relates to a method for automatically teaching parameters to a tray sealer, for example, position values, acceleration values and/or distances. | 12-20-2012 |
20120330865 | SYSTEM AND METHOD FOR FORMULATING A PROBLEM - A method for formulating a problem using a computational system is provided. The method includes determining an initial problem statement that characterizes the problem and identifying a plurality of factors affecting the problem. The method also includes generating a plurality of hypotheses associated with the problem based upon the identified factors and updating the initial problem statement to an updated problem statement using the initial problem statement, identified factors and the plurality of hypotheses. | 12-27-2012 |
20120330866 | SYSTEM AND METHOD FOR DETERMINING AN OPTIMUM QC STRATEGY FOR IMMEDIATE RELEASE RESULTS - The present invention proposes a method for optimizing a quality control strategy for rapid release results. An embodiment of the invention includes generating a set of candidate quality control rules and for each candidate rule, computing a maximum number of patient specimens that can be tested between quality control events while keeping the expected number of correctable unacceptable results below a predetermined correctable maximum and keeping the expected number of final unacceptable results below a predetermined final maximum. Furthermore a quality control utilization rate can be computed based on the number of patient specimens tested between each quality control event and the number of reference samples tested at each quality control event. The candidate rule for which the best quality control utilization rate may be selected along with the corresponding number of patients to be tested between each quality control as the optimum quality control strategy. | 12-27-2012 |
20120330867 | SYSTEMS AND METHODS FOR LARGE-SCALE RANDOMIZED OPTIMIZATION FOR PROBLEMS WITH DECOMPOSABLE LOSS FUNCTIONS - Systems and methods directed toward processing optimization problems using loss functions, wherein a loss function is decomposed into at least one stratum loss function, a loss is decreased for each stratum loss function to a predefined stratum loss threshold individually using gradient descent, and the overall loss is decreased to a predefined threshold for the loss function by appropriately ordering the processing of the strata and spending appropriate processing time in each stratum. Other embodiments and aspects are also described herein. | 12-27-2012 |
20120330868 | MATCHING APPARATUS AND NON-TRANSITORY COMPUTER READABLE STORAGE MEDIUM - The matching apparatus | 12-27-2012 |
20130006896 | Training Datasets for Memory Devices - Methods and systems involve the use of training datasets to determine one or more reference voltages used to read data in a memory unit. Approaches for accessing a memory device having multiple memory units includes storing a training dataset comprising at least one of a known data pattern and a codeword capable of being decoded in a training dataset field of each memory unit of a memory device. One or more reference voltages are determined using the training dataset stored in the memory unit. After the reference voltages have been determined using the training dataset, these reference voltages are used to read other fields of the memory unit. | 01-03-2013 |
20130006897 | PREDICTING USER NAVIGATION EVENTS - A method and system for predicting a next navigation event are described. Aspects of the disclosure minimize the delay between a navigation event and a network response by predicting the next navigation event. The system and method may then prerender content associated with the next navigation event. For example, the method and system may predict a likely next uniform resource locator during web browsing to preemptively request content from the network before the user selects the corresponding link on a web page. The methods describe a variety of manners of predicting the next navigation event, including examining individual and aggregate historical data, text entry prediction, and cursor input monitoring. | 01-03-2013 |
20130006898 | SYSTEM, METHOD AND COMPUTER PROGRAM PRODUCT FOR IDENTITY INFERENCE IN A USER DEVICE - Methods, systems and computer program products to allow a user to identify himself to a content provider, without having to explicitly perform a log in process or other identification and authentication process. By manipulating a user device such as a remote control, a profile of the user may be constructed, where the profile includes a representation of how the individual user typically manipulates the device. The profile includes a feature vector that is a function of the number of times that individual buttons are pressed. The construction of the feature vector may be viewed as a training or learning phase. Once the profile and feature vector are constructed, the user's interaction with the device in a subsequent session may be captured and compared with the profile. This may allow identification of the user, in turn allowing content to be tailored in a manner specific to this user. | 01-03-2013 |
20130006899 | Activity Recognition in Multi-Entity Environments - A physical environment is equipped with a plurality of non-obtrusive sensors (e.g., motion sensors). As a plurality of residents perform various activities within the physical environment, sensor readings are received from one or more of the sensors. Based on the sensor readings, each of the plurality of residents is identified and locations of each of the plurality of residents are tracked. | 01-03-2013 |
20130006900 | Event Prediction Using Hierarchical Event Features - Event prediction using hierarchical event features is described. In an embodiment a search engine monitors search results presented to users and whether users click on those search results. For example, features describing the search result events are universal resource locator prefix levels which are inherently hierarchically related. In an embodiment a graphical data structure is created and stored and used to represent the hierarchical relationships between features. An online training process is used in examples which enables knowledge to be propagated through the graphical data structure according to the hierarchical relations between features. In an example, the graphical data structure is used to predict whether a user will click on a search result and those predictions are used by the search engine to rank search results for future searches. In another example the events are advertisement impressions and the predictions are used by an online advertisement system. | 01-03-2013 |
20130013534 | HARDWARE-ASSISTED APPROACH FOR LOCAL TRIANGLE COUNTING IN GRAPHS - A method and apparatus are provided for hardware-assisted local triangle counting in a graph. The method includes converting vertex relationships of the graph into rule patterns. The method also includes compiling the rule patterns into a binary file, wherein the rule patterns are organized into a finite state machine. The method further includes loading at least a part of the binary file and a search string to be compared there against into a hardware pattern matching accelerator. The method additionally includes receiving a number of matching outputs from the pattern matching accelerator. | 01-10-2013 |
20130013535 | Method for Summarizing Event-Related Texts To Answer Search Queries - A method and apparatus for receiving training data that comprise a plurality of event-and-time-specific texts that are contextually related to a plurality of events; iteratively processing the training data to generate a modified network model that defines a plurality of states; receiving additional data that comprise a plurality of additional event-and-time-specific texts that are contextually related to a particular event; processing the additional data by applying the modified network model to the additional data to identify, within the plurality of additional event-and-time specific texts, a particular set of texts that belong to a particular state of the plurality of states; identifying, within the particular set of texts, one or more texts that are most representative of all texts in the particular set of texts that belong to the particular state; wherein the method is performed by one or more special-purpose computing devices. | 01-10-2013 |
20130013536 | METRIC LEARNING DEVICE, METRIC LEARNING METHOD, AND RECORDING MEDIUM - A metric learning device ( | 01-10-2013 |
20130013537 | CLASSIFICATION METHOD AND APPARATUS - A method and system for building a classification model for classifying documents comprising: representing each of a plurality of documents by a vector of n dimensions, said n dimensions forming a vector space; and representing the classification of already classified documents into classes by separating said vector space into a plurality of subspaces by one or more hyperplanes. | 01-10-2013 |
20130013538 | RECOVERING THE STRUCTURE OF SPARSE MARKOV NETWORKS FROM HIGH-DIMENSIONAL DATA - A method, information processing system, and computer readable article of manufacture model data. A first dataset is received that includes a first set of physical world data. At least one data model associated with the first dataset is generated based on the receiving. A second dataset is received that includes a second set of physical world data. The second dataset is compared to the at least one data model. A probability that the second dataset is modeled by the at least one data model is determined. A determination is made that the probability is above a given threshold. A decision associated with the second dataset based on the at least one data model is generated in response to the probability being above the given threshold. The probability and the decision are stored in memory. The probability and the decision are provided to user via a user interface. | 01-10-2013 |
20130013539 | SYSTEM AND METHOD FOR DOMAIN ADAPTION WITH PARTIAL OBSERVATION - System, method and computer program product provides a novel domain adaption/transfer learning approach applied to the problem of classifying abbreviated documents, e.g., short text messages, instant messages, tweets. The proposed method uses a large number of multi-labeled examples (source domain) to improve the learning on the partial observations (target domain). Specifically, a hidden, higher-level abstraction space is learned that is meaningful for the multi-labeled examples in the source domain. This is done by simultaneously minimizing the document reconstruction error and the error in a classification model learned in the hidden space using known labels from the source domain. The partial observations in the target space are then mapped to the same hidden space, and classified into the label space determined by the source domain. Exemplary results provided for a Twitter dataset demonstrate that the method identifies meaningful hidden topics and provides useful classifications of specific tweets. | 01-10-2013 |
20130013540 | GRAPH-BASED TRANSFER LEARNING - Transfer learning is the task of leveraging the information from labeled examples in some domains to predict the labels for examples in another domain. It finds abundant practical applications, such as sentiment prediction, image classification and network intrusion detection. A graph-based transfer learning framework propagates label information from a source domain to a target domain via the example-feature-example tripartite graph, and puts more emphasis on the labeled examples from the target domain via the example-example bipartite graph. An iterative algorithm renders the framework scalable to large-scale applications. The framework propagates the label information to both features irrelevant to the source domain and unlabeled examples in the target domain via common features in a principled way. | 01-10-2013 |
20130013541 | System And Method For Invitation Targeting In A Web-Based Social Network - A system and method for selecting users of a web-based social network who are likely to respond to an invitation, each of the users having associated profile information is disclosed. The method includes selecting pilot users and a reduced set of keywords from the profile information. The method further includes sending the invitation to the pilot users, receiving responses from the pilot users, and classifying the responses as either positive or negative. A training set of vector pairs is created each vector pair representing a pilot user and including data representing a classified response and training keywords selected from the reduced set of keywords and associated profile information for the pilot user. A function is determined based on the vectors and used to calculate a likelihood that each of one or more users of the web based social network will respond to the invitation. | 01-10-2013 |
20130013542 | SCALABLE TRAFFIC CLASSIFIER AND CLASSIFIER TRAINING SYSTEM - A traffic classifier has a plurality of binary classifiers, each associated with one of a plurality of calibrators. Each calibrator trained to translate an output score of the associated binary classifier into an estimated class probability value using a fitted logistic curve, each estimated class probability value indicating a probability that the packet flow on which the output score is based belongs to the traffic class associated with the binary classifier associated with the calibrator. The classifier training system configured to generate a training data based on network information gained using flow and packet sampling methods. In some embodiments, the classifier training system configured to generate reduced training data sets, one for each traffic class, reducing the training data related to traffic not associated with the traffic class. | 01-10-2013 |
20130018823 | Detecting undesirable content on a social networkAANM Masood; Syed GhouseAACI Kuala LumpurAACO MYAAGP Masood; Syed Ghouse Kuala Lumpur MY - A method of detecting undesirable content on a social networking website. The method includes retrieving or accessing a post from a user's social networking page, identifying the content of a pre-defined set of features of the post, comparing the identified feature content with a database of known undesirable post feature content, and using the results of the comparison to determine whether the post is undesirable. | 01-17-2013 |
20130018824 | SENTIMENT CLASSIFIERS BASED ON FEATURE EXTRACTIONAANM Ghani; RayidAACI ChicagoAAST ILAACO USAAGP Ghani; Rayid Chicago IL USAANM Krema; MarkoAACI EvanstonAAST ILAACO USAAGP Krema; Marko Evanston IL US - Method and apparatus are provided for providing one or more sentiment classifiers from training data using supervised classification techniques based on features extracted from the training data. Training data includes a plurality of units such as, but not limited to, documents, paragraphs, sentences, and clauses. A feature extraction component extracts a plurality of features from the training data, and a feature value determination component determines a value for each extracted feature based on a frequency at which each feature occurs in the training data. On the other hand, a class labeling component labels each unit of the training data according to a plurality of sentiment classes to provide labeled training data. Thereafter, a sentiment classifier generation component provides a least one sentiment classifier based on the value of each extracted feature and the labeled training data using a supervised classification technique. | 01-17-2013 |
20130018825 | DETERMINATION OF A BASIS FOR A NEW DOMAIN MODEL BASED ON A PLURALITY OF LEARNED MODELSAANM GHANI; RayidAACI ChicagoAAST ILAACO USAAGP GHANI; Rayid Chicago IL USAANM Krema; MarkoAACI EvanstonAAST ILAACO USAAGP Krema; Marko Evanston IL US - In a machine learning system in which a plurality of learned models, each corresponding to a unique domain, already exist, new domain input for training a new domain model may be provided. Statistical characteristics of features in the new domain input are first determined. The resulting new domain statistical characteristics are then compared with statistical characteristics of features in prior input previously provided for training at least some of the plurality of learned models. Thereafter, at least one learned model of the plurality of learned models is identified as the basis for the new domain model when the new domain input statistical characteristics compare favorably with the statistical characteristics of the features in the prior input corresponding to the at least one learned model. | 01-17-2013 |
20130018826 | LOCATION DETERMINATION USING GENERALIZED FINGERPRINTINGAANM Sundararajan; ArjunAACI RedmondAAST WAAACO USAAGP Sundararajan; Arjun Redmond WA USAANM Lin; Jyh-HanAACI Mercer IslandAAST WAAACO USAAGP Lin; Jyh-Han Mercer Island WA US - An RF fingerprinting methodology is generalized to include non-RF related factors. For each fingerprinted tile, there is an associated distance function between two fingerprints (the training fingerprint and the test fingerprint) from within that tile which may be a linear or non-linear combination of the deltas between multiple factors of the two fingerprints. The distance function for each tile is derived from a training dataset corresponding to that specific tile, and optimized to minimize the total difference between real distances and predicted distances. Upon receipt of an inference request, a result is derived from a combination of the fingerprints from the training dataset having the least distance per application of the distance function. Likely error for the tile is also determined to ascertain whether to rely on other location methods. | 01-17-2013 |
20130018827 | SYSTEM AND METHOD FOR AUTOMATED LABELING OF TEXT DOCUMENTS USING ONTOLOGIESAANM He; JingruiAACI OssiningAAST NYAACO USAAGP He; Jingrui Ossining NY USAANM Lawrence; Richard D.AACI RidgefieldAAST CTAACO USAAGP Lawrence; Richard D. Ridgefield CT USAANM Melville; PremAACI White PlainsAAST NYAACO USAAGP Melville; Prem White Plains NY USAANM Sindhwani; VikasAACI HawthorneAAST NYAACO USAAGP Sindhwani; Vikas Hawthorne NY USAANM Chenthamarakshan; Vijil E.AACI OssiningAAST NYAACO USAAGP Chenthamarakshan; Vijil E. Ossining NY US - A first mapping function automatically maps a plurality of documents each with a concept of ontology to create a documents-to-ontology distribution. An ontology-to-class distribution that maps concepts in the ontology to class labels, respectively, is received, and a classifier is generated that labels a selected document with an associated class identified based on the documents-to-ontology distribution and the ontology-to-class distribution. | 01-17-2013 |
20130018828 | SYSTEM AND METHOD FOR AUTOMATED LABELING OF TEXT DOCUMENTS USING ONTOLOGIES - A first mapping function automatically maps a plurality of documents each with a concept of ontology to create a documents-to-ontology distribution. An ontology-to-class distribution that maps concepts in the ontology to class labels, respectively, is received, and a classifier is generated that labels a selected document with an associated class identified based on the documents-to-ontology distribution and the ontology-to-class distribution. | 01-17-2013 |
20130024403 | AUTOMATICALLY INDUCED CLASS BASED SHRINKAGE FEATURES FOR TEXT CLASSIFICATION - A method and apparatus are provided for automatically inducing class based shrinkage features. The method includes clustering each word in a set of word groupings of a given type into a respective one of a plurality of classes. The method further includes selecting and extracting a set of class-based shrinkage features from the set of word groupings based on the plurality of classes. The set of class-based shrinkage features is specifically selected for an intended classification application. | 01-24-2013 |
20130024404 | FLIGHT CACHING METHODS AND APPARATUS - According to some aspects, a system is provided comprising at least one computer readable storage medium storing a cache of flight information comprising a plurality of flight solutions, the cache capable of being accessed to obtain flight solutions that meet a criteria specified in one or more flight search queries, and at least one computer programmed to apply at least one machine learning model to at least some of the flight information in the flight information cache to classify at least one of the plurality of flight solutions according to an assessed fidelity of the at least one flight solution, and perform at least one action based on the classified at least one flight solution. | 01-24-2013 |
20130024405 | DISTRIBUTED SCALABLE INCREMENTALLY UPDATED MODELS IN DECISIONING SYSTEMS - In one embodiment, first weight information indicating a first set of delta values is obtained, where the first set of delta values includes a first delta value for each weight in a set of weights, the set of weights including a weight for each of a set of one or more parameters of a model. In addition, second weight information indicating a second set of delta values is obtained, where the second set of delta values includes a second delta value for each weight in the set of weights. Combined weight information including a combined set of delta values or a combined set of weights is generated based, at least in part, upon the first weight information and the second weight information. | 01-24-2013 |
20130024406 | Scalable Ontology Extraction - Techniques for facilitating learning of one or more ontological rules of a resource description framework database are provided. The techniques include obtaining ontology vocabulary from a resource description framework database, generating a rule hypothesis by incrementally building upon a previously learnt rule from the database by adding one or more predicates to the previously learnt rule, performing a constraint check on the generated rule hypothesis by determining compatibility with each previously learnt rule to ensure that a complete rule set including each previously learnt rule and the generated rule hypothesis is consistent, validating the rule hypothesis as a rule using one or more association rule mining techniques to determine validity of the rule hypothesis against the database, and applying the rule to the database to infer one or more facts from the database to facilitate learning of one or more additional ontological rules. | 01-24-2013 |
20130024407 | TEXT CLASSIFIER SYSTEM - The present invention provides a method, and a system, for analysing a textual passage and classifying it against a number of predetermined categories. In the event that the text passage under analysis is not sufficiently similar to any of the predetermined categories then it will be classified as belonging to a further category. | 01-24-2013 |
20130031032 | UTILIZATION OF FEATURES EXTRACTED FROM STRUCTURED DOCUMENTS TO IMPROVE SEARCH RELEVANCE - Features automatically extracted from semi-structured web pages are utilized by a search engine to rank documents that include semi-structured web pages. These features include, but are not limited to, a number of reviews, a number of positive reviews, and/or a number of negative reviews from a web page that includes user reviews. These features also include a number of views of a video that is viewable by way of a semi-structured web page. The features also include a number of subscribers to broadcasts of an individual from a social networking web page and a number of contacts of an individual listed on a social networking web page. | 01-31-2013 |
20130031033 | SYSTEM AND METHOD FOR IMPLEMENTING A LEARNING MODEL FOR PREDICTING THE GEOGRAPHIC LOCATION OF AN INTERNET PROTOCOL ADDRESS - A system and method for implementing a learning model for predicting the geographic location of an Internet Protocol (IP) address are disclosed. A particular embodiment of the system and method includes receiving a model to predict a geographic coordinates position of an Internet Protocol (IP) address, the model including one or more parameters and one or more variables associated with coordinates of the IP address and corresponding information associated with the IP address; receiving training data including a plurality of pairs of coordinates of a target IP address and corresponding information associated with the target IP address; determining, by use of a processor, the one or more parameters based on the training data and the model; and returning a result including information indicative of the determined parameters. | 01-31-2013 |
20130031034 | ADAPTIVE RANKING OF NEWS FEED IN SOCIAL NETWORKING SYSTEMS - Machine learning models are used for ranking news feed stories presented to users of a social networking system. The social networking system divides its users into different sets, for example, based on demographic characteristics of the users and generates one model for each set of users. The models are periodically retrained. The news feed ranking model may rank news feeds for a user based on information describing other users connected to the user in the social networking system. Information describing other users connected to the user includes interactions of the other users with objects associated with news feed stories. These interactions include commenting on a news feed story, liking a news feed story, or retrieving information, for example, images, videos associated with a news feed story. | 01-31-2013 |
20130031035 | LEARNING ADMISSION POLICY FOR OPTIMIZING QUALITY OF SERVICE OF COMPUTING RESOURCES NETWORKS - A system for learning admission policy for optimizing quality of service of computer resources networks is provided herein. The system includes a statistical data extractor configured to extract historical data of deployment requests issued to an admission unit of a computer resources network. The system further includes a Markov decision process simulator configured to generate a simulation model based on the extracted historical data and resources specifications of the computer resources network, in terms of a Markov decision process. The system further includes a value function generator configured to determine a value function for deployment requests admissions. The system further includes a machine learning unit configured to train a classifier based on the simulation model and the value function, to yield an admission policy usable for processing incoming deployment requests. | 01-31-2013 |
20130031036 | PARAMETER SETTING APPARATUS, NON-TRANSITORY MEDIUM STORING COMPUTER PROGRAM, AND PARAMETER SETTING METHOD - A parameter setting apparatus for a control parameter for a wireless communication network including a processor, wherein optimizations for optimizing the control parameter are separated into groups which are unrelated to each other, and the processor executes: a first agent program which are assigned to a group-by-group selects an optimization to be activated according to a first value function; and a second agent program which learns a second value function for determining whether an optimization that affects the first value function is to be activated or not and determines whether the optimization is to be activated or not according to the second value function, and, the activation of the optimization by the first agent program is stopped when the second agent program activates the optimization. | 01-31-2013 |
20130031037 | SYSTEM AND METHODOLOGY PROVIDING AUTOMATION SECURITY ANALYSIS AND NETWORK INTRUSION PROTECTION IN AN INDUSTRIAL ENVIRONMENT - The present invention relates to a system and methodology facilitating automation security in a networked-based industrial controller environment. Various components, systems and methodologies are provided to facilitate varying levels of automation security in accordance with security analysis tools, security validation tools and/or security learning systems. The security analysis tool receives abstract factory models or descriptions for input and generates an output that can include security guidelines, components, topologies, procedures, rules, policies, and the like for deployment in an automation security network. The validation tools are operative in the automation security network, wherein the tools perform security checking and/or auditing functions, for example, to determine if security components are in place and/or in suitable working order. The security learning system monitors/learns network traffic patterns during a learning phase, fires alarms or events based upon detected deviations from the learned patterns, and/or causes other automated actions to occur. | 01-31-2013 |
20130031038 | System and Method For Multiclass Discrimination of Neural Response Data - Systems and methods are described herein for analyzing neural response data that can be assigned to multiple classes. The systems and methods begin with a set of training data from which optimal weight factors are derived. The derived weight factors are used in a classifier which is then applied to test data from test subjects. The classifier filters out the effects of less relevant data in the test data and provides a result in the form of probabilities associated with classes for the test data. | 01-31-2013 |
20130036076 | METHOD FOR KEYWORD EXTRACTION - Presented is a method of extracting keywords. The method includes obtaining a corpus of documents, determining a first set of words that appear as keywords in a document present in the corpus of documents, determining a second set of words that appear in the corpus of documents but not necessarily appear as keywords in the document, and determining a final set of keywords for the document by combining the first set of words with the second set of words. | 02-07-2013 |
20130041856 | METHOD FOR DETECTION OF MOVEMENT OF A SPECIFIC TYPE OF OBJECT OR ANIMAL BASED ON RADAR SIGNALS - A method of detecting movement includes using a radar sensor to monitor a space, and receiving an output signal from the radar sensor. A Fourier transform is performed on the output signal to produce a frequency domain signal spectrum. The frequency domain signal spectrum is transformed into an acoustic domain signal. It is decided whether the output signal is indicative of movement of a predetermined object or a non-human animal dependent upon at least one feature of the acoustic domain signal and at least one spectral feature of the signal spectrum. | 02-14-2013 |
20130046714 | Evaluating the health status of a system - A method and apparatus for determining a health of the system. Groups of vibration data are identified for the system. A group of vibration data in the groups of vibration data comprises data for vibrations of the system at different frequencies over time. The groups of vibration data for the system are stored in a number of associative memories in a computer system. The health of the system is identified based on the groups of vibration data in the number of associative memories. | 02-21-2013 |
20130046715 | METHOD TO DETERMINE AN ARTIFICIAL LIMB MOVEMENT FROM AN ELECTROENCEPHALOGRAPHIC SIGNAL - The present invention is related to a method to determine an artificial limb movement comprising the steps of: providing an EEG input training dataset; providing an output prosthetic limb movement training dataset corresponding to said EEG input training dataset; providing a dynamic recurrent neural network (DRNN) comprising a convergence acceleration algorithm; training said DRNN with said input and output datasets to define synaptic weights W | 02-21-2013 |
20130054494 | EFFICIENT DATA PROFILING TO OPTIMIZE SYSTEM PERFORMANCE - Systems and methods for data profiling are provided. In one embodiment, the method comprises monitoring value of at least a target parameter during execution of logic code in a computing environment, wherein the value of the target parameter is incrementally updated in a sequence of data points; and using statistical analysis to determine a target value for the target parameter as of a current data point, in response to determining a change in the value of the target parameter at each data point. | 02-28-2013 |
20130054495 | ENCODING OF DATA FOR PROCESSING IN A SPATIAL AND TEMPORAL MEMORY SYSTEM - A spatial and temporal memory system (STMS) processes input data to detect whether spatial patterns and/or temporal sequences of spatial patterns exist within the data, and to make predictions about future data. The data processed by the STMS may be retrieved from, for example, one or more database fields and is encoded into a distributed representation format using a coding scheme. The performance of the STMS in predicting future data is evaluated for the coding scheme used to process the data as performance data. The selection and prioritization of STMS experiments to perform may be based on the performance data for an experiment. The best fields, encodings, and time aggregations for generating predictions can be determined by an automated search and evaluation of multiple STMS systems. | 02-28-2013 |
20130054496 | ASSESSING PERFORMANCE IN A SPATIAL AND TEMPORAL MEMORY SYSTEM - A spatial and temporal memory system (STMS) processes input data to detect whether spatial patterns and/or temporal sequences of spatial patterns exist within the data, and to make predictions about future data. The data processed by the STMS may be retrieved from, for example, one or more database fields and is encoded into a distributed representation format using a coding scheme. The performance of the STMS in predicting future data is evaluated for the coding scheme used to process the data as performance data. The selection and prioritization of STMS experiments to perform may be based on the performance data for an experiment. The best fields, encodings, and time aggregations for generating predictions can be determined by an automated search and evaluation of multiple STMS systems. | 02-28-2013 |
20130054497 | SYSTEMS AND METHODS FOR DETECTION OF SATISFICING IN SURVEYS - Response data relating to a plurality of responses to a survey is received. The survey comprises a plurality of questions. The response data for each of the plurality of responses comprises a plurality of answers to at least a subset of the plurality of questions. A questionnaire response model is created using the response data. It is then determined, for each questionnaire response, a respective probability that the respective questionnaire response represents satisficing, such that where the respective probability exceeds a threshold, the respective questionnaire response is identified as an outlier, and where the respective probability does not exceed the threshold, the respective questionnaire response is identified as an inlier. A representation of each questionnaire response is output, each respective representation reflecting the likelihood that the respective response to which the respective representation represents satisficing. | 02-28-2013 |
20130054498 | System and Method For Providing Personalized Recommendations - A system and method for providing a personalized recommendation from a series of partial preferences is presented. A preference distribution of a population including a plurality of weighted ranked lists is identified. A revealed preference of a user is compared to the plurality of ranked lists. An affinity weight between the user and each of the plurality of ranked lists is assigned, and a weighted average of each of the affinity weights is taken. | 02-28-2013 |
20130060723 | METHOD AND SYSTEM FOR A SMART AGENT FOR INFORMATION MANAGEMENT WITH FEED AGGREGATION - The present document describes a system and method for managing electronic information. The system may include an input adapted to receive a search topic from a remote user device over a communication network, and a smart agent module configured to perform a semantic analysis on the topic to generate a set of filter parameters. The system may then to collect electronic information about the search topic from at least one remote electronic information source over the communication network, and filter the collected information using the set of filter parameters to produce a stream of filtered information. The filtered information may then be packaged in discrete information containers and sent to the user device for display. | 03-07-2013 |
20130066814 | System and Method for Automated Classification of Web pages and Domains - Representative sample pages from websites accessible to Internet users are manually selected and classified into pre-defined categories based on page content to create a training set as an input to a classifier. An automated analysis is performed to identify a list of catchwords comprising the most frequently referenced words, tags, and/or links from the classified samples in each category in the training set. A data mining tool generates unique sets of distinctive catchwords and/or distinctive combinations of catchwords that have a high probability of appearing only in a single one of the pre-defined content categories. The classifier utilizes the sets of distinctive catchwords/combinations to classify new pages into one or more of the pre-defined content categories. | 03-14-2013 |
20130066815 | SYSTEM AND METHOD FOR MOBILE CONTEXT DETERMINATION - Methods and a system for mobile device activity classification or context determination. The device compresses and sends sensor data to a remote server together with a selected activity label during a training phase. The remote server receives labeled sensor data from a number of devices and generates a classification model. The model may be reduced to a subspace that represents the dominant model parameters. The subspace data structure, which may be a small matrix, is transmitted to the mobile device. The mobile device uses the subspace data structure to classify device activity as indicated by the device sensors. In one example, the sensor data is projected onto the subspace matrix, which results in estimates of state probabilities for the various predefined states, the dominant one of which is selected as the current state, or estimated state. | 03-14-2013 |
20130066816 | INFORMATION PROCESSING APPARATUS, INFORMATION PROCESSING METHOD AND PROGRAM - Provided is an information processing apparatus including a learning part performing learning of a model of an environment in which an agent performs action, using an observed value observed in the agent when the agent capable of action performs action, an action determining part determining action to be performed by the agent, based on the model, and a user instruction output part outputting instruction information representing an instruction from a user according to the instruction from the user, wherein the action determining part determines the action performed by the agent according to the instruction information when there is an instruction from the user. | 03-14-2013 |
20130066817 | INFORMATION PROCESSING APPARATUS, INFORMATION PROCESSING METHOD AND PROGRAM - There is provided an information processing apparatus including an information amount gain calculation unit that, on the assumption that a factor that could cause a state transition in a state transition model occurs and the state transition takes place as a result of an occurrence of the factor, determines an information amount gain, which is an information amount obtained by causing the factor to occur regarding a causal relationship between the factor and the state transition and observing a result thereof, an evaluation value calculation unit that determines an evaluation value to evaluate effectiveness of causing each of the factors to occur based on the information amount gain to acquire the causal relationship between the factor and the state transition, and a decision unit that decides the factor to be occurred based on the evaluation value. | 03-14-2013 |
20130066818 | Automatic Crowd Sourcing for Machine Learning in Information Extraction - A method for enabling machine learning from unstructured documents is described. The method comprises analyzing at an electronic device, one or more structured databases, thereby providing a mapping between a plurality of referenced character strings and a corresponding plurality of type labels; providing, at the electronic device, a first unstructured document comprising a plurality of unstructured character strings; analyzing the first unstructured document to identify a first character string of the plurality of unstructured character strings which is associated with a first referenced character string of the plurality of referenced character strings; associating, within the first unstructured document, a first type label which is mapped to the first referenced character string to the first character string; and determining a training set for machine learning from the first unstructured document comprising the association to the first type label. | 03-14-2013 |
20130073485 | METHOD AND APPARATUS FOR MANAGING RECOMMENDATION MODELS - A platform for managing recommendation models is described. The platform processes and/or facilitates a processing of at least one user identification characteristic associated with at least one device to determine a user identity. The platform further determines at least one communication account active at the at least one device. The platform also causes, at least in part, an association of one or more recommendations models with the user identity, the at least one communication account, the at least one device, or a combination thereof. | 03-21-2013 |
20130073486 | SYSTEMS AND METHODS FOR ANALYSIS OF NETWORK EQUIPMENT COMMAND LINE INTERFACE (CLI) AND RUNTIME MANAGEMENT OF USER INTERFACE (UI) GENERATION FOR SAME - Systems and methods are disclosed that may be implemented for network management system (NMS) configuration management support for network devices using a learning and natural language processing application to capture the usage and behavior of the Command Line Interface (CLI) of a network device with the aid of a CLI knowledge model, which in one example may be ontology-based. | 03-21-2013 |
20130073487 | METHOD AND APPARATUS FOR UTILIZING USER FEEDBACK TO IMPROVE SIGNIFIER MAPPING - Embodiments disclosed herein may relate to processing a user input comprising a resource identity signifier for a target resource with reference to a heuristic knowledge base utilizing a processor of a computing platform to determine a possible target resource. Embodiments may further relate to learning a social usage of the resource identity signifier from feedback gathered from a plurality of users based at least in part on previous social usage of the resource identity signifier, and may also relate to transmitting a resource locator corresponding to the determined possible target resource to a user computing platform at least in part in response to a determination of the possible target resource having a degree of confidence exceeding a selected threshold. | 03-21-2013 |
20130073488 | METRICS MONITORING AND FINANCIAL VALIDATION SYSTEM (M2FVS) FOR TRACKING PERFORMANCE OF CAPITAL, OPERATIONS, AND MAINTENANCE INVESTMENTS TO AN INFRASTRUCTURE - Techniques for evaluating the accuracy of a predicted effectiveness of an improvement to an infrastructure include collecting data, representative of at least one pre-defined metric, from the infrastructure during first and second time periods corresponding to before and after a change has been implemented, respectively. A machine learning system can receive compiled data representative of the first time period and generate corresponding machine learning data. A machine learning results evaluator can empirically analyze the generated machine learning data. An implementer can implement the change to the infrastructure based at least in part on the data from a machine learning data outputer. A system performance improvement evaluator can compare the compiled data representative of the first time period to that of the second time period to determine a difference, if any, and compare the difference, if any, to a prediction based on the generated machine learning data. | 03-21-2013 |
20130073489 | HYBRID INTERIOR-POINT ALTERNATING DIRECTIONS ALGORITHM FOR SUPPORT VECTOR MACHINES AND FEATURE SELECTION - A method for training a classifier for selecting features in sparse data sets with high feature dimensionality includes providing a set of data items x and labels y, minimizing a functional of the data items x and associated labels y | 03-21-2013 |
20130080358 | ONLINE ASYNCHRONOUS REINFORCEMENT LEARNING FROM CONCURRENT CUSTOMER HISTORIES - In one embodiment, an indication of a Decision Request or an Update Request may be received, where the Update Request is activated independent of user activity. A user state pertaining to at least one user may be received, obtained, accessed or constructed. For the Decision Request, one or more actions may be scored according to one or more value functions associated with a computing device, a policy associated with the computing device may be applied to identify one of the scored actions as a decision, and an indication of the decision may be provided or applied. For the Update Request, the one or more value functions and/or the policy may be updated. An indication of updates to the one or more value functions and/or an indication of updates to the policy may be provided. | 03-28-2013 |
20130080359 | ASSISTING VEHICLE GUIDANCE OVER TERRAIN - A method and system for assisting with guiding a vehicle over terrain is provided. The method includes training at least one first classifier technique using a first set of terrain classifier training data, such that the at least one first classifier technique is trained to output at least one probability value usable to classify terrain. The first trained classifier technique is then used to generate a second set of terrain classifier training data. A second classifier technique is trained using the output of the at least one first classifier technique, and additional data to output a probability value useable to classify terrain. | 03-28-2013 |
20130085970 | INTELLIGENT INTENT DETECTION FROM SOCIAL NETWORK MESSAGES - An intent engine that automatically detects user intent from messages of a social network (e.g., messages with questions to ask) and outputs intent data. The engine is intelligent in that it can process natural language input such as questions and terms. The user is then directed to an answer page filtered according to the intent data and which proviJoshdes answers related to a question, for example. The intent engine can be designated (e.g., tagged, or “friended”) and then linked into a specialized relationship (e.g., a “friend”). Accordingly, in one example, a URL link is constructed that points to the answer page, with filters configured based on the intent data. The URL is then sent back to the user as a friendly response. When the user selects the link, the user is presented with an answer page that provides answers which match the user intent derived from the user messages. | 04-04-2013 |
20130085971 | AUTOMATIC TRACE RETRIEVAL USING SEMANTIC BRIDGE - A method for performing automatic trace retrieval includes receiving a first and second model for a system or service (S | 04-04-2013 |
20130091078 | Method And Apparatus To Determine Rules Implementation Decision - A technique and associated mechanism that guides the user through a set of questions relating to operation rules used in the design of Service Oriented Architecture Systems (SOAs). The questions are related to key aspects of a solution—security, maintenance frequency, usage demand/performance and complexity. Preferably, the questions are yes-or-no questions. Based on the answers provided, an appropriate path will be selected categorize into an appropriate category. The category of the rule will require, or at least suggest, the SOA component into which the rule will be implemented when it is implemented by the SOA designer. the technique is technology specific agnostic and helps in selecting an appropriate tool/platform in a standard and consistent manner. | 04-11-2013 |
20130091079 | USING A HEURISTICALLY-GENERATED POLICY TO DYNAMICALLY SELECT STRING ANALYSIS ALGORITHMS FOR CLIENT QUERIES - A method for dynamically selecting string analysis algorithms can begin with the training of the dynamic string analysis handler of a string analysis module to effectively handle a subset of string queries having contextual metadata received from a client application in an instructional environment. The effectiveness of the training module can be based upon feedback from the client application. Upon completion of the training, a string analysis algorithm selection policy can be synthesized. The string analysis algorithm selection policy can correlate a context of a string query in the subset to the usage of a string analysis algorithm. When in the operational environment, the dynamic string analysis handler can dynamically handle string queries having contextual metadata received from the client application in accordance with the string analysis algorithm selection policy. The string analysis algorithm to be used for a string query can be dynamically and independently determined. | 04-11-2013 |
20130091080 | PROBABILISTIC MODEL CHECKING OF SYSTEMS WITH RANGED PROBABILITIES - Systems and methods for model checking of live systems are shown that include learning an interval discrete-time Markov chain (IDTMC) model of a deployed system from system logs; and checking the IDTMC model with a processor to determine a probability of violating one or more probabilistic safety properties. Checking the IDTMC model includes calculating a linear part exactly using affine arithmetic; and over-approximating a non-linear part using interval arithmetic. | 04-11-2013 |
20130091081 | LATENT FACTOR DEENDENCY STRUCTURE DETERMINATION - Disclosed is a general learning framework for computer implementation that induces sparsity on the undirected graphical model imposed on the vector of latent factors. A latent factor model SLFA is disclosed as a matrix factorization problem with a special regularization term that encourages collaborative reconstruction. Advantageously, the model may simultaneously learn the lower-dimensional representation for data and model the pairwise relationships between latent factors explicitly. An on-line learning algorithm is disclosed to make the model amenable to large-scale learning problems. Experimental results on two synthetic data and two real-world data sets demonstrate that pairwise relationships and latent factors learned by the model provide a more structured way of exploring high-dimensional data, and the learned representations achieve the state-of-the-art classification performance | 04-11-2013 |
20130097102 | SYSTEMS AND METHODS FOR MANAGING PUBLICATION OF ONLINE ADVERTISEMENTS - Exemplary embodiments provide systems, devices, one or more non-transitory computer-readable media and computer-executable methods for managing publication of online advertising. In exemplary embodiments, computer-based publication techniques may include, but is not limited to, automatically determining whether the content of a particular web page article is suitable or unsuitable for accompaniment with one or more advertisements, automatically determining whether an advertisement is suitable or unsuitable for publication on a web page associated with a web page article, and automatically determining a category that may be used to classify the content of a web page article in order to select one or more categories of advertisements suitable for accompaniment with the web page article. | 04-18-2013 |
20130097103 | Techniques for Generating Balanced and Class-Independent Training Data From Unlabeled Data Set - Techniques for creating training sets for predictive modeling are provided. In one aspect, a method for generating training data from an unlabeled data set is provided which includes the following steps. A small initial set of data is selected from the unlabeled data set. Labels are acquired for the initial set of data selected from the unlabeled data set resulting in labeled data. The data in the unlabeled data set is clustered using a semi-supervised clustering process along with the labeled data to produce data clusters. Data samples are chosen from each of the clusters to use as the training data. The selecting, presenting, clustering and choosing steps are repeated with one or more additional sets of data selected from the unlabeled data set until a desired amount of training data has been obtained, wherein at each iteration an amount of the labeled data is increased. | 04-18-2013 |
20130097104 | METHOD AND SYSTEM FOR DOCUMENT CLASSIFICATION - The present invention provides a method for document classification, especially an adaptive learning method for document classification. The document includes a plurality of feature words. The method includes steps of calculating a plurality of similarities between the document and a categorical basic knowledge; calculating a first ratio of a first largest similarity to a second largest similarity of the plurality of similarities; storing the feature words of the document as an extensive categorical knowledge when the first ratio is larger than a first threshold value; and updating the categorical basic knowledge by using the extensive categorical knowledge. | 04-18-2013 |
20130097105 | CONTEXT AWARE APPARATUS AND METHOD - A context aware apparatus is provided. The context aware apparatus includes an extracting unit configured to extract a terminological-box (T-box) from a semantic model, a first generating unit configured to generate a reasoning rule based on the extracted T-box, a second generating unit configured to generate a first assertion-box (A-box) based on sensing information, and a reasoning unit configured to infer a user context based on the reasoning rule and the first A-box. | 04-18-2013 |
20130097106 | APPARATUS AND METHOD FOR EXTENDING A DEFAULT MODEL OF A TERMINAL - Provided are an apparatus and method for extending a default model of a terminal. The apparatus may extend a default model of the terminal using an extension model relating to the default model and using linked data. | 04-18-2013 |
20130097107 | INFORMATION PROCESSING APPARATUS, INFORMATION PROCESSING METHOD, AND PROGRAM - Provided is an information processing apparatus including: a reward estimator generating unit using action history data, which includes state data expressing a state of an agent, action data expressing an agent's action, and a reward value expressing a reward of the action, as learning data to generate, through machine learning, a reward estimator estimating the reward value from inputted state data and action data; an action selecting unit preferentially selecting an action not included in the action history data but with a high estimated reward value; and an action history adding unit causing the agent to perform the selected action and adding to the action history data the state data and action data for the action and the action's reward value in association with each other. The reward estimator is regenerated when a set of state data, action data, and the reward value is added to the action history data. | 04-18-2013 |
20130097108 | Two-Stage Multiple Kernel Learning Method - Disclosed are methods and structures of Multiple Kernel learning framed as a standard binary classification problem with additional constraints that ensure the positive definiteness of the learned kernel. Advantageously, the disclosed methods and structures permit the use of binary classification technologies to develop better performing, and more scalable Multiple Kernel Learning methods that are conceptually simpler. | 04-18-2013 |
20130103617 | Computer-Implemented Systems And Methods For Forecasting And Estimation Using Grid Regression - Systems and methods are provided for estimating a value for a target variable. A plurality of known entities are assigned to cells of a grid, where the known entities are assigned to the cells based upon attribute data. A determination is made as to whether each cell has at least a threshold number of assigned known entities. When one of the cells contains fewer than the threshold number of known entities, cells are combined to form a super cell. A model is generated for each cell and super cell based upon target variable values for known entities assigned to that cell or super cell. Data for a target entity is received, and the target entity is assigned to one the cells. One of the models is selected based upon the cell assignment, and an estimate is generated for the target variable for the target entity using the selected model. | 04-25-2013 |
20130103618 | DECISION MAKING WITH ANALYTICALLY COMBINED SPLIT CONDITIONS - Systems, methods, and other embodiments associated with decision making with analytically combined split conditions are provided. In one embodiment, a method for classifying data is provided. An input data sample is received for classification as belonging to one of two possible classes. The input data sample includes a set of attribute values. The method includes evaluating the set of attribute values with a tree function that defines a decision boundary of a classification tree. The tree function classifies an input data sample as belonging to one of the two possible classes based, at least in part, on the attribute values of the input data sample. In another embodiment parameters of the tree function are derived by applying a gradient descent parameter update rule to the training data samples. | 04-25-2013 |
20130103619 | COMPOSITE PRODUCTION RULES - A method for forming and using a composite production rule may include compiling, by a computer system, a decision table or a decision tree to generate a composite production rule. The method may also include generating the composite production rule and selecting, by the computer system, an algorithm for compiling the composite production rule. The method may additionally include compiling, by the computer system, the composite production rule into an executable program based on pattern matching of the selected algorithm. The method may further include executing, by the computer system, the composite production rule to provide an output based on the composite production rule. | 04-25-2013 |
20130103620 | FEATURE VECTOR CLASSIFICATION DEVICE AND METHOD THEREOF - Disclosed is a feature vector classification device which includes an initial condition setting unit; a variable calculating unit configured to receive a training vector and to calculate an error and a weight according to setting of the initial condition setting unit; a loop deciding unit configured to determine whether re-calculation is required, based on a comparison result between the calculated error and an error threshold; and a hyperplane generating unit configured to generate a hyperplane when an end signal is received from the loop deciding unit. | 04-25-2013 |
20130103621 | INTELLIGENT CONTROLLER PROVIDING TIME TO TARGET STATE - The current application is directed to intelligent controllers that continuously, periodically, or intermittently calculate and display the time remaining until a control task is projected to be completed by the intelligent controller. In general, the intelligent controller employs multiple different models for the time behavior of one or more parameters or characteristics within a region or volume affected by one or more devices, systems, or other entities controlled by the intelligent controller. The intelligent controller collects data, over time, from which the models are constructed and uses the models to predict the time remaining until one or more characteristics or parameters of the region or volume reaches one or more specified values as a result of intelligent controller control of one or more devices, systems, or other entities. | 04-25-2013 |
20130103622 | AUTOMATED CONTROL-SCHEDULE ACQUISITION WITHIN AN INTELLIGENT CONTROLLER - The current application is directed to intelligent controllers that initially aggressively learn, and then continue, in a steady-state mode, to monitor, learn, and modify one or more control schedules that specify a desired operational behavior of a device, machine, system, or organization controlled by the intelligent controller. An intelligent controller generally acquires one or more initial control schedules through schedule-creation and schedule-modification interfaces or by accessing a default control schedule stored locally or remotely in a memory or mass-storage device. The intelligent controller then proceeds to learn, over time, a desired operational behavior for the device, machine, system, or organization controlled by the intelligent controller based on immediate-control inputs, schedule-modification inputs, and previous and current control schedules, encoding the desired operational behavior in one or more control schedules and/or sub-schedules. | 04-25-2013 |
20130103623 | Computer-Implemented Systems and Methods for Detection of Sentiment in Writing - Systems and methods are provided for the detection of sentiment in writing. A plurality of texts is received from a larger collection of writing samples with a computer system. A set of seed words from the plurality of texts are labeled as being of positive sentiment or of negative sentiment with the computer system. The set of seed words is expanded in size with the computer system to provide an expanded set of seed words. Intensity values are assigned to words of the expanded set of seed words. Each of the words of the expanded set of seed words is assigned three intensity values: a value corresponding to the strength of the word's association with a positive polarity class, a value corresponding to the strength of the word's association with a negative polarity class, and a value corresponding to the strength of the word's association with a neutral polarity class. | 04-25-2013 |
20130103624 | Method and system for estimating response to token instance of interest - Estimating a response to a token instance of interest, including the steps of: receiving token instances to which a user was exposed, receiving a total response of the user to the token instances; receiving attention levels of the user in the token instances; selecting the token instance of interest from among the token instances based on the attention level; and estimating the response to the token instance of interest from the total response. | 04-25-2013 |
20130103625 | INFORMATION PROCESSING APPARATUS AND METHOD, AND PROGRAM THEREOF - There is provided an information processing apparatus including: evaluation information extracting means extracting evaluation information from evaluation of every user for an item; preference information creating means for creating preference information indicating a preference of every user on the basis of the evaluation information extracted by the evaluation information extracting means and an item characteristic amount indicating a characteristic of the item; space creating means for creating a space in which the user is located, according to the preference information; and display control means for controlling display of the user located in the space, according to the space created by the space creating means and the preference information. The apparatus may be applied to, for example, an image display apparatus which displays server images for providing a variety of items and information. | 04-25-2013 |
20130110745 | RULE GENERATION FOR EVENT PROCESSING SYSTEM | 05-02-2013 |
20130110746 | IDENTIFICATION OF ENTITIES LIKELY TO ENGAGE IN A BEHAVIOR | 05-02-2013 |
20130110747 | RELATIONAL LEARNING FOR SYSTEM IMITATION | 05-02-2013 |
20130110748 | Policy Violation Checker | 05-02-2013 |
20130110749 | CONTROL DEVICE AND METHOD FOR CALCULATING AN OUTPUT PARAMETER FOR A CONTROLLER | 05-02-2013 |
20130110750 | ONLINE TEMPORAL DIFFERENCE LEARNING FROM INCOMPLETE CUSTOMER INTERACTION HISTORIES | 05-02-2013 |
20130117202 | KNOWLEDGE-BASED DATA QUALITY SOLUTION - The subject disclosure relates to a knowledge-driven data quality solution that is based on a rich knowledge base. The data quality solution can provide continuous improvement and can be based on continuous (or on-going) knowledge acquisition. The data quality solution can be built once and can be reused for multiple data quality improvements, which can be for the same data or for similar data. The disclosed aspects are easy to use and focus on productivity and user experience. Further, the disclosed aspects are open and extendible and can be applied to cloud-based reference data (e.g., a third party data source) and/or user generated knowledge. According to some aspects, the disclosed aspects can be integrated with data integration services. | 05-09-2013 |
20130117203 | DOMAINS FOR KNOWLEDGE-BASED DATA QUALITY SOLUTION - The subject disclosure relates to a knowledge-driven data quality solution that is based on a rich knowledge base. The data quality solution can provide continuous improvement and can be based on continuous (or on-going) knowledge acquisition. The data quality solution can be built once and can be reused for multiple data quality improvements, which can be for the same data or for similar data. The disclosed aspects are easy to use and focus on productivity and user experience. Further, the disclosed aspects are open and extendible and can be applied to cloud-based reference data (e.g., a third party data source) and/or user generated knowledge. According to some aspects, the disclosed aspects can be integrated with data integration services. | 05-09-2013 |
20130117204 | INFERRING PROCEDURAL KNOWLEDGE FROM DATA SOURCES - A procedural inference system is described herein that infers procedural knowledge from various data sources to help a user complete one or more tasks for which the data sources provide information. The system understands users' queries, identifies a task at hand, provides recommendations on the steps to take and the agents to use based on a knowledge base of tasks and agents, and provides the fabric to determine which different agents can work together to help the user accomplish a task. Tasks can be started on one device and completed on another seamlessly. Users are able to finish complex, multi-step tasks efficiently, without trial and error or data reentry. Thus, the procedural inference system provides a generalized framework that helps users to complete tasks using already available data and does not ask each data provider to invest in infrastructure to build dedicated task information systems. | 05-09-2013 |
20130117205 | METHOD OF IDENTIFYING A PROTOCOL GIVING RISE TO A DATA FLOW - Method of identifying a protocol at the origin of a data flow. The method of identifying a protocol giving rise to a packet flow comprises the following steps: a capture of the flow of the protocol to be identified, statistical classification of the flow, comprising an extraction of the classification parameters and a comparison of the classification parameters with statistical models constructed during a learning phase. The statistical classification comprises: a first phase of global statistical classification; and a step of synthesis of the results of the first and second classification phases so as to identify the protocol giving rise to the flow. | 05-09-2013 |
20130124436 | Profiling Energy Consumption - Embodiments for detecting anomalous consumption of energy are provided. Information associated with energy consumption over a designated period of time is received. A threshold value is received. A classifier based on an Auto-Regressive Moving Average model is applied to the information and a result representing the likelihood of an attack is determined. The result is then analyzed to determine if it attained a threshold value. The information is then classified as indicating an attack. Additionally, embodiments for utilizing machine learning to train a classifier using training data to develop parameters for the auto-regressive moving average model are provided. Further, embodiments for evaluating the effectiveness of the parameters used in the Auto-Regressive Moving Average model to classify data are provided. | 05-16-2013 |
20130124437 | SOCIAL MEDIA USER RECOMMENDATION SYSTEM AND METHOD - Each user is represented by a mixture of topics, e.g., one or more topics, and a probability of interest in each topic in the mixture, and given the target user, one or more other users can be recommended, each user that is recommended to the target user is determined to have a topical interest similarity with the target user, e.g., the target user's interest in one or more topics of the mixtures of topics is determined to be similar to a recommended interest in the one or more topics of the mixture of topics. The target user and the one or more recommended users can be said to have similar topical interests. The target user can use the user recommendation to establish an interactive dialogue, for example, with one or more users identified in the user recommendation. | 05-16-2013 |
20130124438 | METHOD OF RECOGNIZING PATTERNS BASED ON MARKOV CHAIN HIDDEN CONDITIONAL RANDOM FIELD MODEL - Provided is a method of recognizing patterns based on a hidden conditional random fields model to which full-Gaussian covariance has been applied. The method includes dividing a training input signal and outputting a frame sequence, extracting a feature vector from the frame sequence, calculating a parameter through a conditional random fields model to which Gaussian covariance has been applied using the feature vector, receiving, by the hidden conditional random fields model to which the parameter has been applied, a feature vector extracted from a test input signal measured for an actual pattern to infer a label indicating the actual pattern, and proposing a method of calculating gradient values for a conditional probability vector, a transition probability vector, a Gaussian mixture weight, a mean of Gaussian distributions, and covariance of the Gaussian distributions, as an analysis method. | 05-16-2013 |
20130124439 | INFORMATION EXTRACTION SYSTEM, METHOD, AND PROGRAM - An information extraction system includes: solution request sentence set acquisition means for acquiring a sentence set matching a positive example solution request pattern which represents a positive example of a sentence including a problem evoking expression and a sentence set matching a negative example solution request pattern representing an opposite request to the positive example solution request, from a corpus respectively as a positive example solution request sentence set and a negative example solution request sentence set, and associating parts, in the positive example solution request sentence set and the negative example solution request sentence set, that correspond to the problem evoking expression in the positive example solution request pattern and the negative example solution request pattern, with a positive example and a negative example; and identification information specification means for comparing, for each problem evoking expression, constituent elements of sentences included in the positive example solution request sentence set and the negative example solution request sentence set, and specifying a constituent element characterizing the positive example solution request sentence set and a constituent element characterizing the negative example solution request sentence set respectively as positive example identification information and negative example identification information. | 05-16-2013 |
20130132308 | Enhanced DeepQA in a Medical Environment - A DeepQA engine is enhanced to provide a digital medical investigation tool which assists a medical professional in researching potential causes of a set of patient conditions, including clues, facts and factoids about the patient. The DeepQA engine provides one or more answers to a natural language question with confidence levels for each answer. If a confidence level falls below a threshold, the enhanced DeepQA engine performs a crowd sourcing operation to gather additional information from one or more domain experts. The domain expert responses are provided to the medical professional, and are learned by the enhanced DeepQA system to provide for better research of similar patient conditions in future queries. | 05-23-2013 |
20130132309 | Method Performed in a Computer System for Aiding the Assessment of an Influence of a User in or Interacting with a Communication System by Applying Social Network Analysis, SNA, Functions, a Computer System, Computer Program and Computer Program Product - The invention relates to a method performed in a computer system for aiding the assessment of an influence of a user in or interacting with a communication system by applying social network analysis, SNA, functions. The method comprises: obtaining two or more SNA metrics for each user of a first number of users, each SNA metric being determined by a respective SNA function; calculating a weight parameter for each one of the SNA metrics using a machine learning method, the weight parameters indicating a combination of the SNA metrics for use in the assessment of the influence of the user; and applying the estimated weight parameters to SNA metrics of a second number of users to assess a ranking in accordance with influence of users in the second number of users. The invention also relates to a computer system, computer programs, and computer program products. | 05-23-2013 |
20130132310 | Method and System for Evaluating the Class of a Test Datum in a Large-Dimension Data Space - A method and a system for evaluating the class of a test datum in an original metric space, each datum belonging to at least one class grouping a plurality of data, includes a step of graphical representation of the spatial organization of a set of learning data of the original space in a representation metric space, a conjoint membership level indicating if any two data from the learning set belong to the same class. The method also includes a step of relating the test datum to the projections of the data from the learning set, the most probable class of the test datum being the class of the projections of the data from the learning set related to the test datum. Application: assistance with decision-making in discrimination, shape recognition. | 05-23-2013 |
20130132311 | SCORE FUSION AND TRAINING DATA RECYCLING FOR VIDEO CLASSIFICATION - Multiple classifiers can be applied independently to evaluate images or video. Where there are heavily imbalanced class distributions, a local expert forest model for meta-level score fusion for event detection can be used. Performance variations of classifiers in different regions of a score space can be adapted. Multiple pairs of experts based on different partitions, or “trees,” can form a “forest,” balancing local adaptivity and over-fitting. Among ensemble learning methods, stacking with a meta-level classifier can be used to fuse an output of multiple base-level classifiers to generate a final score. A knowledge-transfer framework can reutilize the base-training data for learning the meta-level classifier. By recycling the knowledge obtained during a base-classifier-training stage, efficient use can be made of all available information, such as can be used to achieve better fusion and better overall performance. | 05-23-2013 |
20130138585 | Methods, Systems, And Computer Program Products For Recommending Applications Based On User Interaction Patterns - A method for recommending an application includes obtaining an input model representing user interaction patterns during execution of a first application. The input model is compared to a reference model representing user interaction patterns during execution of a second application. A similarity is determined between the input model and the reference model. A recommendation of the second application is generated in response to the similarity. | 05-30-2013 |
20130138586 | SERVICE GOAL INTERPRETING APPARATUS AND METHOD FOR GOAL-DRIVEN SEMANTIC SERVICE DISCOVERY - A service goal interpreting apparatus for goal-driven semantic service discovery is provided. The service goal interpreting apparatus includes a goal interpretation unit that interprets a goal of at least one service or application provided on the Web, and a goal registration unit that registers the goal interpreted by the goal interpretation unit in a service registry. | 05-30-2013 |
20130138587 | SYSTEM AND METHOD FOR GRAPH PATTERN ANALYSIS - In some example embodiments, a system and method are provided for graph pattern analysis. In example embodiments, pattern data of a primary network that includes data relating to relationships between entities are received. A secondary network based on the pattern data of the primary network is generated by using an algorithm that processes pattern characteristics extracted from the pattern data. The generated secondary network is provided for further analysis. | 05-30-2013 |
20130138588 | Identifying and ranking networked biographies and referral paths corresponding to selected qualifications - The most common automated search methods produce less-than-ideal results when searching online resumes, profiles, and the like (“biographies”) for the identities of people with a searcher-selected qualification (“candidates”). Keywords, their proximities, and their repetitions are less informative in biographies than in other informational documents. Similarly, chains of social connection (“referral paths”) do not always reveal the likelihood or ease of a searcher's introduction to a candidate. In both cases, the display order of results may be unrelated to any estimate of merit. To answer the question “Whom do I need and how do I reach them?” a classifier system uses heuristics or algorithms adapted to match the reactions of human experts on the selected qualifications. Terms in biographies, regardless of structure, are standardized and disambiguated for accurate comparisons, meaningful context is preserved, and biographies and referral paths are scored based on expected usefulness to the searcher. | 05-30-2013 |
20130144812 | PROBABILISTIC MODEL APPROXIMATION FOR STATISTICAL RELATIONAL LEARNING - Various technologies described herein pertain to approximating an inputted probabilistic model for statistical relational learning. An initial approximation of formulae included in an inputted probabilistic model can be formed, where the initial approximation of the formulae omits axioms included in the inputted probabilistic model. Further, an approximated probabilistic model of the inputted probabilistic model can be constructed, where the approximated probabilistic model includes the initial approximation of the formulae. Moreover, the approximated probabilistic model and evidence can be fed to a relational learning engine, and a most probable explanation (MPE) world can be received from the relational learning engine. The evidence can comprise existing valuations of a subset of relations included in the inputted probabilistic model. The MPE world can include valuations for the relations included in the inputted probabilistic model. The MPE world can be outputted when the input probabilistic model lacks an axiom violated by the MPE world. | 06-06-2013 |
20130144813 | Analyzing Data Sets with the Help of Inexpert Humans to Find Patterns - A combined computer/human approach is used to detect actionable insights in large data sets. Automated computer analysis used to identify patterns (e.g., possibly meaningful patterns or subsets within the data). These are presented to humans for feedback, where the humans may have little to no training in the statistical methods used to detect actionable insights. Feedback from the humans is used to improve the pattern detection and facilitate the detection of actionable insights. | 06-06-2013 |
20130144814 | CONDITIONAL PROBABILITY OPERATOR FOR EVENT PROCESSING SYSTEMS - An event processing system in which a computer receives an input event comprising one or more factors. The computer evaluates the factors of the input event based on an event processing rule containing a pattern detection operator and a conditional probability operator. The conditional probability operator can operate to calculate a conditional probability for a set of training data that a specified pattern will appear in the factors of an input event given a specified output event, and can further operate to assign a conditional rule value a binary value based on how the conditional probability compares to a target probability. | 06-06-2013 |
20130144815 | MAKING PREDICTIONS REGARDING EVALUATION OF FUNCTIONS FOR A DATABASE ENVIRONMENT - A prediction regarding one or more functions can be made for a database environment. In particular, a predication can be made with respect to values stored in at least one column of at least one table in a database, based on the evaluation of one or more functions for a subset of possible column values (i.e., resultant values derived from the evaluation of a subset of possible column values) without the need to calculate the function(s) for all of the actual entries in the column of the table(s). In effect, a functional predicate can be transformed (or translated) to a predicate that is dependent on the column values instead of the evaluation of one or more functions for the column values. | 06-06-2013 |
20130144816 | HEALTH CARE INCIDENT PREDICTION - Embodiments described herein relate to apparatuses and methods for incident prediction alerts for transmission to a health care organization system by applying rules to data sets. Each rule may define a set of data elements linked to an incident, and a processor may detect one or more sets of data elements in the data sets. The processor may normalize the data feeds, generate rules on historical data, generate prediction alerts by applying rules to near-real time data feeds, train to update rules, validate and error check rules, remove statistical noise, generate visualizations for the data feeds, and receive input and feedback data. | 06-06-2013 |
20130144817 | Parallel training of a Support Vector Machine (SVM) with distributed block minimization - A method to solve large scale linear SVM that is efficient in terms of computation, data storage and communication requirements. The approach works efficiently over very large datasets, and it does not require any master node to keep any examples in its memory. The algorithm assumes that the dataset is partitioned over several nodes on a cluster, and it performs “distributed block minimization” to achieve the desired results. Using the described approach, the communication complexity of the algorithm is independent of the number of training examples. | 06-06-2013 |
20130144818 | NETWORK INFORMATION METHODS DEVICES AND SYSTEMS - Methods and systems for predicting links in a network, such as a social network, are disclosed. The existing network structure can be used to optimize link prediction. The methods and systems can learn a distance metric and/or a degree preference function that are structure preserving to predict links for new/existing nodes based on node properties. | 06-06-2013 |
20130144819 | SCORE NORMALIZATION - Methods, systems, and apparatus, including computer programs encoded on computer storage media, for score normalization. One of the methods includes receiving initial training data, the initial training data comprising initial training records, each initial training record identifying input data as input and a category as output. The method includes generating a first trained predictive model using the initial training data and a training function. The method includes generating intermediate training records by inputting input data of the initial training records to a second trained predictive model, the second trained predictive model generated using the training function, each intermediate training record having a score. The method also includes generating a score normalization model using a score normalization training function and the intermediate training records. | 06-06-2013 |
20130144820 | METHOD OF LEARNING A CONTEXT OF A SEGMENT OF TEXT, AND ASSOCIATED HANDHELD ELECTRONIC DEVICE - An improved method of learning a context of a segment of text input enables facilitated text input on an improved handheld electronic device. In response to a series of inputs, segments and other objects are analyzed to generate a proposed character interpretation of the series of inputs. Responsive to detecting a replacement of a segment of the character interpretation with another segment, a combination object comprising the another segment and a preceding object is stored. In response to another series of inputs, the combination object can be employed by a processing algorithm to ascertain a preference for the another segment in the context of the preceding object of the combination object. | 06-06-2013 |
20130151440 | METHOD OF REGENERATING DIFFRACTION SIGNALS FOR OPTICAL METROLOGY SYSTEMS - Provided is a method for enhancing accuracy of an optical metrology system that includes a metrology tool, an optical metrology model, and a profile extraction algorithm. The optical metrology model includes a model of the metrology tool and a profile model of the sample structure, the profile model having profile parameters. A library comprising Jones and/or Mueller matrices and/or components (JMMOC) and corresponding profile parameters is generated using ray tracing and a selected range of beam propagation parameters. An original simulated diffraction signal is calculated using the optical metrology model. A regenerated simulated diffraction signal is obtained using the regenerated JMMOC, integrated for all the rays of the optical metrology model. If an error and precision criteria for the regenerated simulated diffraction signal compared to the original simulated diffraction signal are met, one or more profile parameters are determined from the best match regenerated simulated diffraction signal. | 06-13-2013 |
20130151441 | MULTI-TASK LEARNING USING BAYESIAN MODEL WITH ENFORCED SPARSITY AND LEVERAGING OF TASK CORRELATIONS - Multi-task regression or classification includes optimizing parameters of a Bayesian model representing relationships between D features and P tasks, where D≧1 and P≧1, respective to training data comprising sets of values for the D features annotated with values for the P tasks. The Bayesian model includes a matrix-variate prior having features and tasks dimensions of dimensionality D and P respectively. The matrix-variate prior is partitioned into a plurality of blocks, and the optimizing of parameters of the Bayesian model includes inferring prior distributions for the blocks of the matrix-variate prior that induce sparseness of the plurality of blocks. Values of the P tasks are predicted for a set of input values for the D features using the optimized Bayesian model. The optimizing also includes decomposing the matrix-variate prior into a product of matrices including a matrix of reduced rank in the tasks dimension that encodes correlations between tasks. | 06-13-2013 |
20130151442 | METHOD FOR LEARNING TASK SKILL AND ROBOT USING THEREOF - Provided are a method for learning task skill and a robot using the same. The modeling method for learning a task skill includes: receiving training data for a task to be performed by a learning engine; dividing, by the learning engine, the received training data into segments by using a geometric property of a predetermined probabilistic model; and learning, by the learning engine, a basis skill for the divided segments by modeling each divided segment. | 06-13-2013 |
20130151443 | Systems and methods for performing contextual classification using supervised and unsupervised training - Computerized systems and methods are disclosed for performing contextual classification of objects using supervised and unsupervised training. In accordance with one implementation, content reviewers may review training objects and submit supervised training data for preprocessing and analysis. The supervised training data may be preprocessed to identify key terms and phrases, such as by stemming, tokenization, or n-gram analysis, and form vectorized objects. The vectorized objects may be used to train one or more models for subsequent classification of objects. In certain implementations, preprocessing or training, among other steps, may be performed in parallel over multiple machines to improve efficiency. The disclosed systems and methods may be used in a wide variety of applications, such as article classification and content moderation. | 06-13-2013 |
20130151444 | METHODS AND APPARATUS FOR UTILISING SOLUTIONS TO SAT PROBLEMS - Computer implemented method to indicate whether a CNF sentence representing a physical system is satisfiable. The method includes structuring a search tree based upon received data representing the CNF sentence. The search tree includes a root node and a plurality of other nodes. The method includes causing the computer to use a search to visit nodes using a decision heuristic at each node to determine which of the branches of the search tree to explore from that node, determining which nodes lie on the solution path, modifying the decision heuristic according to the analysis, generating a trained decision heuristic, and using the trained decision heuristic to process CNF sentences to determine whether those CNF sentences are satisfiable. A shortest path through the search tree provides a solution path and the heuristic can be trained with a set of training instances. | 06-13-2013 |
20130151445 | Method and System for Survival of Data Plane Through a Total Control Plane Failure - A system and method for retaining routes in a control plane learned by an inter-domain routing protocol in the event of a connectivity failure between routers. Routers are classified as either route reflectors or originators. A determination is made whether the connectivity failure occurred between a route reflector and an originator, two originators, or two route reflectors. A determination is then made whether to propagate a withdrawal of learned routes based on whether the connectivity failure occurred between a route reflector and an originator, two originators, or two route reflectors. A withdrawal of learned routes is propagated to neighboring routers if the connectivity failure occurred between two originators, or between a route reflector and an originator that is inaccessible via an intra-domain routing protocol. No withdrawal of learned routes is propagated if the connectivity failure occurred between two route reflectors, or between a route reflector and an originator that is accessible via an intra-domain routing protocol. | 06-13-2013 |
20130151446 | METHOD AND SYSTEM FOR DETERMINING THE ACCURACY OF DNA BASE IDENTIFICATIONS - Embodiments disclosed herein relate to a method and system for determining the accuracy of DNA base identifications, based at least partly on sampling characteristics of subsets within training data sets. | 06-13-2013 |
20130151447 | METHOD AND APPARATUS FOR SELF-LEARNING AND SELF-IMPROVING A SEMICONDUCTOR MANUFACTURING TOOL - Performance of a manufacturing tool is optimized. Optimization relies on recipe drifting and generation of knowledge that capture relationships among product output metrics and input material measurement(s) and recipe parameters. Optimized recipe parameters are extracted from a basis of learned functions that predict output metrics for a current state of the manufacturing tool and measurements of input material(s). Drifting and learning are related and lead to dynamic optimization of tool performance, which enables optimized output from the manufacturing tool as the operation conditions of the tool changes. Features of recipe drifting and associated learning can be autonomously or externally configured through suitable user interfaces, which also can be drifted to optimize end-user interaction. | 06-13-2013 |
20130159219 | Predicting the Likelihood of Digital Communication Responses - Different advantageous embodiments provide for response prediction. A social element is received by a prediction mechanism. A feature set is generated for the social element. A prediction is generated using the feature set and a prediction model. | 06-20-2013 |
20130159220 | PREDICTION OF USER RESPONSE ACTIONS TO RECEIVED DATA - A system is provided for automatically predicting actions a user is likely to take in response to receiving data. The system may be configured to monitor and observe a user's interactions with incoming data and to identify patterns of actions the user may take in response to the incoming data. The system may enable a trainer component and a classifier component to determine the probability a user may take a particular action and to make predictions of likely user actions based on the observations of the user and the identified pattern of the user's actions. The system may also be configured to continuously observe the user's actions to fine-tune and adjust the identified patterns of user actions and to update the probabilities of likely user actions in order increase the accuracy of the predicted user action in response to incoming data. | 06-20-2013 |
20130159221 | Systems for Monitoring Computer Resources - One embodiment of a system of the present invention for monitoring computer resources includes means for retrieving a set of resource-metric records for a predetermined time interval, means for forming a first mathematical matrix containing metric's values arranged on date-time and resource-metric axes, means for creating a second mathematical matrix containing features and a third mathematical matrix containing weights, means for building a feature relationship tree, means for generating a predicted value for the resource-metric identifier, means for determining a variance between predicted value and metric's value, and means for triggering an alert if the variance exceeds a predetermined alert threshold. | 06-20-2013 |
20130159222 | INTERACTIVE INTERFACE FOR OBJECT SEARCH - Editorial curation of search results includes: receiving a search results page rendered in response to a search query; receiving user edits to the search results page, the user edits including changes to objects in the search results page; and applying the user edits to the search results page. | 06-20-2013 |
20130159223 | Virtual Sensor Development - Embodiments include processes, systems, and devices for developing a virtual sensor. The virtual sensor includes one or more inference models. A decision engine utilizes an inference model associated with a mobile device to determine another inference model that is configured to accept physical sensor data from another mobile device. In this way, the virtual sensor can be developed for use with many mobile devices using initial inference models developed for a small number of mobile devices or a single mobile device. Embodiments also include methods to select mobile devices from which to request physical sensor data for virtual sensor input. Embodiments also include architectures that provide a library of virtual sensors. | 06-20-2013 |
20130159224 | TECHNIQUES FOR REAL-TIME CUSTOMER PREFERENCE LEARNING - Techniques for real-time offer customer preference learning are presented. Local agents on communication channels are equipped with predefined rules that capture actions and behaviors of customers interacting with an enterprise. The metrics associated with these actions and behaviors are plugged into the rules and in some cases combined with known pre-existing preferences for the customers for purposes of evaluating the rules and creating newly learned preferences for the customers. The newly learned preferences are dynamically fed into offer evaluation processing to determine whether to make offers to the customers. | 06-20-2013 |
20130159225 | MODEL BASED CALIBRATION OF INFERENTIAL SENSING - An inferential sensor module is incorporated into an engine simulation model. One or more parameters for the inferential sensor module are calibrated using one or more of engine measurement data and the engine simulation model. The calibration is performed such that a difference between an inferred signal predicted by the inferential sensor module and a signal measured on an engine is minimized. The inferential sensor module and the one or more calibrated parameters are loaded into an engine control unit in order to predict inferred variables. | 06-20-2013 |
20130159226 | Method for Screening Samples for Building Prediction Model and Computer Program Product Thereof - A method for screening samples for building a prediction model and a computer program product thereof are provided. When a set of new sample data is added to a dynamic moving window (DMW), a clustering step is performed with respect to all of the sets of sample data within the window for grouping the sets of sample data with similar properties as one group. If the number of the sets of sample data in the largest group is greater than a predetermined threshold, it means that there are too many sets of sample data with similar properties in the largest group, and the oldest sample data in the largest group can be deleted; if smaller than or equal to a predetermined threshold, it means that the sample data in the largest group are quite unique, and should be kept for building or refreshing the prediction model. | 06-20-2013 |
20130159227 | CLUSTERING COOKIES FOR IDENTIFYING UNIQUE MOBILE DEVICES - Embodiments are directed towards clustering cookies for identifying unique mobile devices for associating activities over a network with a given mobile device. The cookies are clustered based on a Bayes Factor similarity model that is trained from cookie features of known mobile devices. The clusters may be used to determine the number of unique mobile devices that access a website. The clusters may also be used to provide targeted content to each unique mobile device. | 06-20-2013 |
20130166480 | NAVIGATION SYSTEM WITH POINT OF INTEREST CLASSIFICATION MECHANISM AND METHOD OF OPERATION THEREOF - A method of operation of a navigation system includes: generating a training data from a randomly sampled uncategorized point of interest; generating a trained classifier model by training a classifier model using the training data; generating a category identifier, a confidence score, or a combination thereof for an uncategorized point of interest using the trained classifier model; generating a categorized point of interest by assigning the category identifier to the uncategorized point of interest; calculating a weighted confidence score based on a weighted F-measure for the category identifier, a pair of the category identifier and the confidence score; and consolidating the categorized point of interest based on the weighted confidence score for the category identifier being meeting or exceeding a threshold for displaying on a device. | 06-27-2013 |
20130166481 | DISCRIMINATIVE DECISION TREE FIELDS - A tractable model solves certain labeling problems by providing potential functions having arbitrary dependencies upon an observed dataset (e.g., image data). The model uses decision trees corresponding to various factors to map dataset content to a set of parameters used to define the potential functions in the model. Some factors define relationships among multiple variable nodes. When making label predictions on a new dataset, the leaf nodes of the decision tree determine the effective weightings for such potential functions. In this manner, decision trees define non-parametric dependencies and can represent rich, arbitrary functional relationships if sufficient training data is available. Decision trees training is scalable, both in the training set size and by parallelization. Maximum pseudolikelihood learning can provide for joint training of aspects of the model, including feature test selection and ordering, factor weights, and the scope of the interacting variable nodes used in the graph. | 06-27-2013 |
20130166482 | METHOD FOR DETERMINING A CORRECTION CHARACTERISTIC CURVE - A method for determining a correction characteristic curve for adapting a characteristic curve of an injection system, in which the correction characteristic curve includes at least one deviation of a measured characteristic curve from a setpoint characteristic curve, the at least one deviation including a sum tolerance of at least two components of the injection system, which have an effect on the characteristic curve. | 06-27-2013 |
20130173503 | COMPOUND SELECTION IN DRUG DISCOVERY - Methods and systems for determining the selection criteria that in its embodiments can distinguish compounds that successfully meet an objective from those that do not, determine the importance of selection criterion in selecting test compounds that have a high probability of achieving an objective and automatically apply the selection criteria to select test compounds with a high chance of meeting an objective. | 07-04-2013 |
20130173504 | SYSTEMS AND METHODS FOR ACTION RECOGNITION - Systems provided herein include a learning environment and an agent. The learning environment includes an avatar and an object. A state signal corresponding to a state of the learning environment includes a location and orientation of the avatar and the object. The agent is adapted to receive the state signal, to issue an action capable of generating at least one change in the state of the learning environment, to produce a set of observations relevant to a task, to hypothesize a set of action models configured to explain the observations, and to vet the set of action models to identify a learned model for the task. | 07-04-2013 |
20130173505 | System and Method For Artificial Lift System Analysis - A system, method, and computer program product are disclosed for analysis of artificial lift systems. An Artificial Lift Analysis Solution (ALAS) is also provided to view and analyze artificial lift well data trends, prediction and detection event alerts, and to diagnose system conditions to facilitate production optimization. Production well information is provided for a plurality of the production wells each being associated with an artificial lift system. Artificial lift system failure alerts for the plurality of production wells are received and processed on a computer. A relevance measure for each of the artificial lift system failure alerts is determined responsive to the production well information. A summary of the artificial lift system failure alerts is displayed in an ordering based on the relevance measure. | 07-04-2013 |
20130173506 | HYBRID LOCATION USING PATTERN RECOGNITION OF LOCATION READINGS AND SIGNAL STRENGTHS OF WIRELESS ACCESS POINTS - A query device scans radio frequencies for visible transmitting devices. The querying device receives at least a signal strength and identifier information associated with each of the transmitting devices. The list of visible devices is used to query a database containing location information for a plurality of visible devices. The list may be sent to a locationing system that may perform a location analysis on the resulting data to return a location to the query device. The weighted average of the locations returned in the database query may be computed to determine the location of the querying device, with the weight for each of the locations being the current signal strength detected by the querying device. Neural network analysis may also be used to determine the location of the querying device. Learning and seeding operations many also be used to populate the database with location information for transmitting devices. | 07-04-2013 |
20130173507 | ADAPTIVE CUSTOMIZED PRESENTATION OF BUSINESS INTELLIGENCE INFORMATION - In one example, a method includes receiving information on a user role of a user account associated with a business intelligence system. The method further includes gathering information on interactions of the user account with the business intelligence system. The method further includes generating an initial business intelligence output based on data from one or more data sources. The method further includes generating a customized business intelligence output for the user account based on the initial business intelligence output, the user role, and the interactions of the user account with the business intelligence system. The method further includes providing the customized business intelligence output to the user account. | 07-04-2013 |
20130173508 | DEFECT CLASSIFICATION APPARATUS - The present invention has its objective to provide a defect classification apparatus which suppresses over-fitting and accurately classify the defect type of a defect. A defect classification apparatus is provided in which a data point indicating feature information of a defect to be classified having an unknown defect type is mapped to a point in a mapping space having a dimensional number higher than the number of features constituting the feature information, and the defect type of the defect to be classified is classified based on in which of two regions of defect type, which are formed by separating the mapping space by a decision boundary, the mapped point is located, wherein a discriminant function indicating the decision boundary is determined by adopting a weight which minimizes the sum of the classification error, which corresponds to the accuracy in classifying a training defect dataset, and a regularization term, which has a positive correlation with the dimensional number of the decision boundary, as the weight for each feature constituting the discriminant function. | 07-04-2013 |
20130173509 | METHOD AND ARRANGEMENT FOR PROCESSING DATA - A method and arrangement for processing data when training a data model involving multiple iterations of data records in a dataset ( | 07-04-2013 |
20130179375 | Signal Detection Algorithms to Identify Drug Effects and Drug Interactions - An algorithm according to an embodiment of the present invention provides for latent signal detection of adverse events. Embodiments infer the presence of adverse drug events from large observational databases housed by the FDA, WHO, and other governmental organizations. The disclosed algorithms do not require the adverse event to be reported explicitly. Instead, the algorithms infer the presence of adverse events through more common secondary effects. In an embodiment, machine learning techniques are used for this purpose. | 07-11-2013 |
20130185230 | MACHINE-LEARNING BASED CLASSIFICATION OF USER ACCOUNTS BASED ON EMAIL ADDRESSES AND OTHER ACCOUNT INFORMATION - A trust level of an account is determined at least partly based on a degree of the memorability of an email address associated with the account. Additional features such as those based on the domain of the email address and those from the additional information such as name, phone number, and address associated with the account may also be used to determine the trust level of the account. A machine learning process may be used to learn a classification model based on one or more features that distinguish a malicious account from a benign account from training data. The classification model is used to determine a trust level of the account, and/or if the account is malicious or benign, and may be continuously improved by incrementally adapting or improving the model with new accounts. | 07-18-2013 |
20130185231 | PREDICTING DIAGNOSIS OF A PATIENT - Method, system, and computer program product are provided for predicting diagnosis of a patient performed by a computerized device. The method may include: modeling data from a group of successfully diagnosed patients, wherein the data is modeled as treatment paths of patients including referrals to medical practitioners; and predicting diagnosis for a current patient by comparing a treatment path of the current patient with the modeled treatment paths of successfully diagnosed patients, including calculating a probability of a given diagnosis from the modeled treatment paths. The method may include: defining a set of medical entities including medical practitioners to which a patient has been referred; and gathering treatment paths of successfully diagnosed patients, wherein the treatment path links medical entities in a directional route. Predicting diagnosis for a current patient may use the modeled data to calculate the probability of each model instance for each diagnosis and choosing the model instance of the diagnosis that maximizes the treatment path probability. | 07-18-2013 |
20130185232 | PROBABILISTIC EVENT NETWORKS BASED ON DISTRIBUTED TIME-STAMPED DATA - Described herein are techniques for producing probabilistic event networks (Bayesian network based representation of node dependencies, whereas nodes comprise event occurrences, explicit times of occurrences, and the context of event occurrences) based on distributed time-stamped data. An aspect provides a method for predicting events from event log data via constructing a probabilistic event net and using the probabilistic event net to infer a probabilistic statement regarding a future event using a network inference mechanism. Other embodiments are disclosed. | 07-18-2013 |
20130185233 | SYSTEM AND METHOD FOR LEARNING POSE CLASSIFIER BASED ON DISTRIBUTED LEARNING ARCHITECTURE - A system and method for learning a pose classifier based on a distributed learning architecture. A pose classifier learning system may include an input unit to receive an input of a plurality of pieces of learning data, and a plurality of pose classifier learning devices to receive an input of a plurality of learning data sets including the plurality of pieces of learning data, and to learn each pose classifier. The pose classifier learning devices may share learning information in each stage, using a distributed/parallel framework. | 07-18-2013 |
20130191310 | PREDICTION MODEL REFINEMENT FOR INFORMATION RETRIEVAL SYSTEM - A learning system refines a prediction model that determines the effectiveness of a search engine in achieving a goal of a search. Search goal achievements are estimated for sequences of user actions in an unlabeled data set using the prediction model, which is based on a mixture model and values for parameters of the mixture model. The values of the parameters are redefined based at least on the search goal achievement estimates of the unlabeled set. The prediction model is stored in accordance with the mixture model and the redefined values. | 07-25-2013 |
20130191311 | OPTIMIZING ELECTRONIC COMMUNICATION CHANNELS - A method, computer program product, and system for electronic communication is described. A first unified telephony session is selected. A first arbitrator associated with the first session is selected. A first set of participants associated with the first session is selected. The first arbitrator is directed to act as a proxy connection for a first channel associated with the first set of participants. | 07-25-2013 |
20130198113 | METHOD AND TECHNIQUE TO CREATE SINGLE INTELLIGENT COLLABORATION PLATFORM SPANNING ACROSS WEB, MOBILE AND CLOUD - A method that knits together and logically sequences diverse services such as, but not limited to, social networks, financial services, news feeds, email services, calendar services, analytical platforms and Business-to-consumer (B2C) services to create a state full cohesive end-to-end user experience on a single intelligent collaboration platform spanning across web, mobile and cloud. | 08-01-2013 |
20130198114 | Classifying Activity Using Probabilistic Models - A method, an apparatus and an article of manufacture for classifying customer activity in an automated customer support system. The method includes obtaining input from the automated customer support system, wherein the input comprises an observable measurement of customer activity in the automated customer support system, computing a probability that the input corresponds to one of one or more probabilistic models, and using the computed probability to classify the customer activity in the automated customer support system by considering the probabilistic model corresponding to a highest computed probability. | 08-01-2013 |
20130198115 | CLUSTERING CROWDSOURCED DATA TO CREATE AND APPLY DATA INPUT MODELS - The collection and clustering of data input characteristics from a plurality of computing devices is provided. The clustered data input characteristics define user groups to which users are assigned. Input models such as language models and touch models are created for, and distributed to, each of the user groups based on the data input characteristics of the users assigned thereto. For example, an input model may be selected for a computing device based on a current context of the computing device. The selected input model is applied to the computing device during the current context to alter the interpretation of input received from the user via the computing device. | 08-01-2013 |
20130198116 | LEVERAGING USER-TO-TOOL INTERACTIONS TO AUTOMATICALLY ANALYZE DEFECTS IN IT SERVICES DELIVERY - An approach is presented for identifying related problem tickets in an information technology (IT) environment. User interactions with a computer program are stored. The user interactions include inputs to the computer program to search for problem tickets issued in the IT environment that have the same characteristics. One or more user interaction patterns within the user interactions are recognized. A user interaction pattern of the one or more user interaction patterns is selected based on an evaluation of effectiveness of each of the one or more user interaction patterns. Based on the user interaction pattern, a rule is generated for determining which problem tickets in the IT environment share a common characteristic. The rule is applied to additional problem tickets issued in the IT environment to identify which of the additional problem tickets share the common characteristic. | 08-01-2013 |
20130198117 | SYSTEMS AND METHODS FOR SEMANTIC DATA INTEGRATION - Embodiments of the present invention relate to a system for data integration and information retrieval by bringing semantically related data together for a given context. As described, the integration of data may include the building of an ontology, the mapping of one or more processes, semantic maps and concept dictionaries in the ontology to one or more data sources, tagging the data sources in accordance with the ontology, providing a query interface for accepting an input query from a user, the mapping of the input query to one or more concepts in the ontology, and deriving one or more subqueries thereby, and the querying of data sources in accordance with the composed one or more subqueries, wherein the data sources queried are tagged with one or more concepts from the ontology. Additionally, the tracking of data across data sources in accordance with a defined data value chain is disclosed. | 08-01-2013 |
20130198118 | ANNOTATION OF A BIOLOGICAL SEQUENCE - A computer-implemented method for annotation of a biological sequence, comprising: applying a classifier to determine a label for the first segment of a first biological sequence of a first species based on an estimated relationship between second segments in a training set and known labels of the second segments in the training set. The classifier is trained using the training set to estimate the relationship, and the second segments are of a second biological sequence of a second species that is different to, or a variant of, the first species. This disclosure also concerns a computer program and a computer system for annotation of a biological sequence. | 08-01-2013 |
20130198119 | APPLICATION OF MACHINE LEARNED BAYESIAN NETWORKS TO DETECTION OF ANOMALIES IN COMPLEX SYSTEMS - According to one embodiment, in response to a set of data for anomaly detection, a Bayesian belief network (BBN) model is applied to the data set, including for each of a plurality of features of the BBN model, performing an estimate using known observed values associated with remaining features to generate a posterior probability for the corresponding feature. A scoring operation is performed using a predetermined scoring algorithm on posterior probabilities of all of the features to generate a similarity score, wherein the similarity score represents a degree to which a given event represented by the data set is novel relative to historical events represented by the BBN model. | 08-01-2013 |
20130198120 | SYSTEM AND METHOD FOR PROFESSIONAL CONTINUING EDUCATION DERIVED BUSINESS INTELLIGENCE ANALYTICS - The present disclosure relates to non-linear analytics engine derived business intelligence. More particularly, the present disclosure describes methods and systems that use content associated with a medical professional continuing education event as a data source for a non-linear analytics engine. The content, which relates to the content creation, content delivery, follow-ups, evaluations, attendee interactions, and administrative tasks associated with medical professional continuing education event, is extracted from a learning management system and subsequently transmitted to the non-linear analytics engine to create business intelligence. | 08-01-2013 |
20130204808 | Fault Prediction of Monitored Assets - Systems and methods for fault prediction are described to reduce equipment failure by effectively monitoring equipment, removing anomalous data, and reducing false alarms. Such systems and methods can be used to receive monitoring data, extract information from the data, and combine extracted information for establishing prediction models. Additionally, fault probabilities may be quantified and faults may be predicted based on the probabilities. | 08-08-2013 |
20130204809 | ESTIMATION OF PREDICTIVE ACCURACY GAINS FROM ADDED FEATURES - Various technologies described herein pertain to estimating predictive accuracy gain of a potential feature added to a set of features, wherein an existing predictor is trained on the set of features. Outputs of the existing predictor for instances in a dataset can be retrieved from a data store. Moreover, a predictive accuracy gain estimate of a potential feature added to the set of features can be measured as a function of the outputs of the existing predictor for the instances in the dataset. The predictive accuracy gain estimate can be measured without training an updated predictor on the set of features augmented by the potential feature. | 08-08-2013 |
20130204810 | DISCRIMINANT MODEL LEARNING DEVICE, METHOD AND PROGRAM - To provide a discriminant model learning device capable of efficiently learning a discriminant model on which domain knowledge indicating user's knowledge or analysis intention for a model is reflected while keeping fitting to data. | 08-08-2013 |
20130204811 | OPTIMIZED QUERY GENERATING DEVICE AND METHOD, AND DISCRIMINANT MODEL LEARNING METHOD - To provide an optimized query generating device capable of generating an optimized query to be given with domain knowledge when generating a discriminant model on which the domain knowledge indicating user's knowledge or analysis intention for a model is reflected. | 08-08-2013 |
20130204812 | METHOD FOR COMPUTER-AIDED CLOSED-LOOP AND/OR OPEN-LOOP CONTROL OF A TECHNICAL SYSTEM - A method for computer-aided closed and/or open-loop control of a technical system is provided. A first value of an output quantity is predicted on a data-based model at a current point in time. A second value of the output quantity is determined from an analytical model. The state of the technical system at the current point is assigned a confidence score in the correctness of prediction of the data-based model. A third value of the output quantity is determined from the first and second value as a function of the confidence score for controlling the technical system. A suitable value for the output quantity can be derived from the analytical model even for regions of the technical system in which the quality of prediction of the data-based model is low because of a small set of training data. The technical systems can be turbines, such as gas turbines. | 08-08-2013 |
20130204813 | SELF-LEARNING, CONTEXT AWARE VIRTUAL ASSISTANTS, SYSTEMS AND METHODS - A virtual assistant learning system is presented. A monitoring device, a cell phone for example, observes user interactions with an environment by acquiring sensor data. The monitoring device uses the sensor data to identify the interactions, which in turn is provided to an inference engine. The inference engine leverages the interaction data and previously stored knowledge elements about the user to determine if the interaction exhibits one or more user preferences. The inference engine can use the preferences and interactions to construct queries targeting search engines to seek out possible future interactions that might be of interest to the user. | 08-08-2013 |
20130212047 | MULTI-TIERED APPROACH TO E-MAIL PRIORITIZATION - An apparatus for automating a prioritization of an incoming message, including a batch learning module that generates a global classifier based on training data that is input to the batch learning module. A feedback learning module that generates a user-specific classifier based on a plurality of feedback instances. A feature extraction module that receives the incoming message and a topic-based user model, infers a topic of the incoming message based on the topic-based user model, and computes a plurality of contextual features of the incoming message. A classification module that dynamically determines a priority classification strategy for assigning a priority level to the incoming message based on the plurality of contextual features of the incoming message and a weighted combination of the global classifier and the user-specific classifier, and classifies the incoming message based on the priority classification strategy. | 08-15-2013 |
20130212048 | METHOD OF performing REAL-TIME CORRECTION OF A WATER STAGE FORECAST - A method of performing real-time correction of a water stage forecast includes obtaining at least one predicted water stage of at least one time and a predicted water stage of a next time after the at least one time; obtaining at least one observed water stage of the at least one time; generating a system error of the water stage forecast according to the at least one observed water stage, the at least one predicted water stage, the predicted water stage of the next time, a Time Series method, and an Average Deviation method; utilizing a Kalman filter method to generate a random error of the water stage forecast; generating a water stage forecast correction of the next time according to the system error and the random error; and correcting a predicted water stage of the next time according to the water stage forecast correction and the predicted water stage. | 08-15-2013 |
20130212049 | Machine Evolutionary Behavior by Embedded Collaborative Learning Engine (eCLE) - This patent develops and demonstrates the technology required for constructing machine evolutionary behavior within systems to enable evolving learning capability for autonomous recognition of new emerging behaviors. A purpose of this technology is to provide a formal methodology and implementation for adding new knowledge, which results from the automated recognition of new patterns (behaviors) within systems. Key characteristic of the “Machine Evolutionary Behavior by Embedded Collaborative Learning engine” consist on operating with an ensemble of learning paradigms, which when instantiated work in a collaborative way. The resulting framework compiles the inherent advantages of the involved methods, but also a synergetic behavior is obtained when working in a collaborative fashion. | 08-15-2013 |
20130212050 | AGENT APPARATUS FOR VEHICLE, AGENT SYSTEM, AGENT COLTROLLING METHOD, TERMINAL APPARATUS AND INFORMATION PROVIDING METHOD - An agent apparatus for a vehicle, an agent controlling method, a terminal apparatus and an information providing method, for executing a communication function with a personified agent, are provided. The apparatus includes an observing part that observes a driving situation based on sensor information; a learning part that learns by storing an observation result obtained from the observing part together with the sensor information; a determining part that determines a communication action with a user based on a learning result obtained from the learning part; a display control part that displays a first image in the vehicle expressing the communication action determined by the determining part; and an obtaining part that obtains acquired information acquired from the outside of the vehicle and stored in a portable terminal apparatus. The determining part may also determine the communication action by reflecting the acquired information on the learning result. | 08-15-2013 |
20130218813 | CLASSIFICATION RELIABILITY PREDICTION - A method, apparatus and product useful for classification reliability prediction. The method being a computer-implemented method performed by a processor, the method comprising: obtaining a prediction of a label for a dataset made by a classifier tool, wherein the classifier tool is aimed at predicting the label based on a classification model and in view of a set of features defining the dataset; obtaining a reliability prediction of a reliability label relating to the prediction of the classifier tool based on a reliability classifier tool, wherein the reliability classifier tool is aimed at predicting the reliability label based on a classification model and in view of a second set of features; and outputting to a user the label prediction and an associated reliability prediction. | 08-22-2013 |
20130218814 | METHOD AND SYSTEM FOR THE DYNAMIC ALLOCATION OF RESOURCES BASED ON FAIRNESS, THROUGHPUT, AND USER BEHAVIOR MEASUREMENT - A system and method for the dynamic allocation of resources based on fairness, throughput, and user behavior measurement. A resource allocation decision can be made based on an index value computed by a selection index function, A fairness coefficient and a throughput coefficient, which represents the significance of fairness and throughput can be computed utilizing a reinforcement learning algorithm and the degree of fairness and throughput coefficient can be varied while allocating resources. A user behavior coefficient with respect to a user can be computed to determine the degree of cooperativeness of the user with other users and the value of user behavior coefficient can be updated each time it interacts with the system. | 08-22-2013 |
20130218815 | METHODS AND SYSTEMS FOR FEATURE EXTRACTION - A method and system for extracting feature utilizing an AHaH module (Anti-Hebbian and Hebbian). A sparse input data stream can be presented to a synaptic matrix of a collection of AHaH nodes associated with the AHaH module. The AHaH module operates an AHaH plasticity rule via an evaluate phase and a feedback phase cycle. A bias input line can be modulated such that a bias weight do not receive a Hebbian portion of the weight update during the feedback phase in order to prevent occupation of a null state. The input space can be bifurcated when the AHaH nodes fall randomly into an attractor state. The output of the AHaH module that forms a stable bit pattern can then be provided as an input to a content-addressable memory for generating a maximally efficient binary label. | 08-22-2013 |
20130218816 | APPARATUS AND METHOD FOR PROCESSING SENSOR DATA IN SENSOR NETWORK - In a sensor network, a sensor data processing apparatus generates a feature vector identifier table by classifying feature vector identifiers of a plurality of situation information determination reference data to be a reference of situation determination according to a sensor type index and a feature vector identifier set index of the plurality of situation information reference data. When the sensor data processing apparatus receives sensor data, the sensor data processing apparatus generates a feature vector identifier of the sensor data and extracts a sensor type index and a feature vector identifier set index of a feature vector identifier most similar to the feature vector identifier of sensor data with reference to a feature vector identifier table, and generates situation recognition information using the extracted sensor type index and feature vector identifier set index. | 08-22-2013 |
20130218817 | ACTIVE ACQUISITION OF PRIVILEGED INFORMATION - A method for active learning using privileged information is disclosed. A processing device receives a set of labeled examples and a set of unlabeled examples. For each unlabeled example in the set of unlabeled examples, the processing device determines whether to query at least one of an oracle to obtain a label for the unlabeled example or a teacher to obtain privileged information about the unlabeled example. The processing device outputs a decision rule based on minimizing a number of queries to the oracle for a label and the teacher for privileged information. Minimizing the number of queries to the teacher and the oracle is based on a cost of querying the teacher or the oracle. | 08-22-2013 |
20130218818 | CROSS CHANNEL OPTIMIZATION SYSTEMS AND METHODS - The inventive subject matter is generally directed to a cross channel optimization system, methods, and related software which provide for the conducting of experiments and/or optimization of digital content across a plurality of external content systems to user of the external content systems. | 08-22-2013 |
20130218819 | SYSTEM AND METHOD TO ESTIMATE REGION OF TISSUE ACTIVATION - A computer-implemented method for determining the volume of activation of neural tissue. In one embodiment, the method uses one or more parametric equations that define a volume of activation, wherein the parameters for the one or more parametric equations are given as a function of an input vector that includes stimulation parameters. After receiving input data that includes values for the stimulation parameters and defining the input vector using the input data, the input vector is applied to the function to obtain the parameters for the one or more parametric equations. The parametric equation is solved to obtain a calculated volume of activation. | 08-22-2013 |
20130226837 | Content Pre-fetching for Computing Devices - The subject disclosure is directed towards a technology that timely pre-fetches content to a computing device based upon a prediction that a user will be requesting access to the content. Features comprising temporal features, spatial features, spatiotemporal features and/or other features associated with content are provided to a model trained at least in part with historical access data. The model returns information from which a determination of whether to pre-fetch the content is made. | 08-29-2013 |
20130226838 | MISSING VALUE IMPUTATION FOR PREDICTIVE MODELS - Provided are techniques for imputing a missing value for each of one or more predictor variables. Data is received from one or more data sources. For each of the one or more predictor variables, an imputation model is built based on information of a target variable; a type of imputation model to construct is determined based on the one or more data sources, a measurement level of the predictor variable, and a measurement level of the target variable; and the determined type of imputation model is constructed using basic statistics of the predictor variable and the target variable. The missing value is imputed for each of the one or more predictor variables using the data from the one or more data sources and one or more built imputation models to generate a completed data set. | 08-29-2013 |
20130226839 | ROBUST BAYESIAN MATRIX FACTORIZATION AND RECOMMENDER SYSTEMS USING SAME - In a recommender method, Bayesian Matrix Factorization (BMF) is performed on a matrix having user and item dimensions and matrix elements containing user ratings for items made by users in order to train a probabilistic collaborative filtering model. A recommendation is generated for a user using the probabilistic collaborative filtering model. The recommendation may comprise a predicted item rating, or an identification of one or more recommended items. The recommender method is suitably performed by an electronic data processing device. The BMF may employ non-Gaussian priors, such as Student-t priors. The BMF may additionally or alternatively employ a heteroscedastic noise model comprising priors that include (1) a row dependent variance component that depends upon the matrix row and (2) a column dependent variance component that depends upon the matrix column. | 08-29-2013 |
20130226840 | Deriving a Nested Chain of Densest Subgraphs from a Graph - A nested chain of densest subgraphs is derived by a computer from a given graph that has multiple vertices and edges. The two ends of each edge are assigned with respective incident weights, and each vertex is given a vertex weight. A weight balancing process is carried out by the computer to iteratively go through the edges to adjust the incident weights of each edge and the vertex weights of the vertices connected by that edge to reduce a difference between the vertex weights of the two vertices. After the balancing, the vertex weights are put in an ordered sequence according to their values, and a nested chain of densest subgraphs is derived from the ordered sequence. | 08-29-2013 |
20130226841 | EXTRACTION OF INFORMATION FROM CLINICAL REPORTS - A method for extracting information from electronic documents, including: learning terms and term variants from a training corpus, wherein the terms and the term variants correspond to a specialized dictionary related to the training corpus; generating a list of negative indicators found in the training corpus; performing a partial match of the terms and the term variants in a set of electronic documents to create initial match results; and performing a negation test using the negative indicators and a positive terms test using the terms and the term variants on the initial match results to remove matches from the initial match results that fail either the negation test or the positive terms test, resulting in final match results. | 08-29-2013 |
20130226842 | MISSING VALUE IMPUTATION FOR PREDICTIVE MODELS - Provided are techniques for imputing a missing value for each of one or more predictor variables. Data is received from one or more data sources. For each of the one or more predictor variables, an imputation model is built based on information of a target variable; a type of imputation model to construct is determined based on the one or more data sources, a measurement level of the predictor variable, and a measurement level of the target variable; and the determined type of imputation model is constructed using basic statistics of the predictor variable and the target variable. The missing value is imputed for each of the one or more predictor variables using the data from the one or more data sources and one or more built imputation models to generate a completed data set. | 08-29-2013 |
20130226843 | EXTRACTION OF INFORMATION FROM CLINICAL REPORTS - A method for extracting information from electronic documents, including: learning terms and term variants from a training corpus, wherein the terms and the term variants correspond to a specialized dictionary related to the training corpus; generating a list of negative indicators found in the training corpus; performing a partial match of the terms and the term variants in a set of electronic documents to create initial match results; and performing a negation test using the negative indicators and a positive terms test using the terms and the term variants on the initial match results to remove matches from the initial match results that fail either the negation test or the positive terms test, resulting in final match results. | 08-29-2013 |
20130226844 | Content Summarizing and Search Method and System Using Thinking System - The present invention relates to a system and method for information process using artificially constructed apparatus. In one preferred embodiment of the present invention, if the task for the system of the present invention is to summarize document content, the thinking mode will first analyze the identification information of the element files for words (or phrases) in the document to identify key words (or phrases), then the key words (or phrases) will be analyzed to establish links between key words (phrases). By the frequency of appearances of the key words (or phrases), in combination of the appearances of other key words (or phrases) that are related to the key words, the most important key word (or phrase) or key words (or phrases) can be obtained. Thus the document can be summarized by the most important key word (or phrase) or key words (or phrases) as the topic (or topics) of the document. | 08-29-2013 |
20130226845 | Instruction System with Eyetracking-Based Adaptive Scaffolding - A digital instructional environment leverages an infrared eye-tracker to monitor a learner's reading and viewing of text and simulations for subject matter. The system detects out-of-order reading/viewing patterns that could lead to poor comprehension. The digital learning environment communicates with other tutorial components including simulation environments, pedagogical agents and may respond in real-time to such patterns with messages that guide learners (knowledge acquirers) to return to effective reading/viewing patterns so as to promote effective construction of mental model(s) developed during knowledge acquisition/learning. | 08-29-2013 |
20130226846 | System and Method for Universal Translating From Natural Language Questions to Structured Queries - A computer implemented question answering (QA) system and method is provided that automatically finds one or more accurate and concise answers for a natural language question. An automated training routine is provided that includes learning a proper mapping from the natural language question to one or more structured queries by discovering and summarizing parallel semantics between a knowledge base and pairs of a natural language question and its answer. The system and method generate as output concise texts answering the natural language question intended by a user. | 08-29-2013 |
20130226847 | METHOD AND SYSTEM FOR MACHINE COMPREHENSION - The AKOS (Artificial Knowledge Object System) of the invention is a software processing engine that relates incoming information to pre-existing stored knowledge in the form of a world model and, through a process analogous to human learning and comprehension, updates or extends the knowledge contained in the model, based on the content of the new information. Incoming information can come from sensors, computer to computer communication, or natural human language in the form of text messages. The software creates as an output. Intelligent action is defined as an output to the real-world accompanied by an alteration to the internal world model which accurately reflects an expected, specified outcome from the action. These actions may be control signals across any standard electronic computer interface or may be direct communications to a human in natural language. | 08-29-2013 |
20130232093 | IMPACT ANALYSIS SYSTEMS AND METHODS - In one exemplary implementation, a system includes an input/output interface for receiving first, second, and third portions of data from first, second and third nodes respectively. The three portions of data contain three respective metrics. The system further includes a processor that is used to generate a primary traversal graph for analyzing interactions between the first, second and third nodes. The analyzing includes designating the first node as an independent node upon detecting that the first metric is an independent metric, and designating a combination of at least the second and third nodes as an integrated node upon detecting a circular interdependency that includes the second and third metrics. The processor is further used to generate a secondary traversal graph for analyzing interactions between the independent node and the integrated node and to determine a cost impact associated with the independent node and/or the integrated node. | 09-05-2013 |
20130232094 | MACHINE LEARNING FOR POWER GRID - A machine learning system for ranking a collection of filtered propensity to failure metrics of like components within an electrical grid that includes a raw data assembly to provide raw data representative of the like components within the electrical grid; (b) a data processor, operatively coupled to the raw data assembly, to convert the raw data to more uniform data via one or more data processing techniques; (c) a database, operatively coupled to the data processor, to store the more uniform data; (d) a machine learning engine, operatively coupled to the database, to provide a collection of propensity to failure metrics for the like components; (e) an evaluation engine, operatively coupled to the machine learning engine, to detect and remove non-complying metrics from the collection of propensity to failure metrics and to provide the collection of filtered propensity to failure metrics; and (f) a decision support application, operatively coupled to the evaluation engine, configured to display a ranking of the collection of filtered propensity to failure metrics of like components within the electrical grid. | 09-05-2013 |
20130232095 | RECOGNIZING FINGER GESTURES FROM FOREARM EMG SIGNALS - A machine learning model is trained by instructing a user to perform various predefined gestures, sampling signals from EMG sensors arranged arbitrarily on the user's forearm with respect to locations of muscles in the forearm, extracting feature samples from the sampled signals, labeling the feature samples according to the corresponding gestures instructed to be performed, and training the machine learning model with the labeled feature samples. Subsequently, gestures may be recognized using the trained machine learning model by sampling signals from the EMG sensors, extracting from the signals unlabeled feature samples of a same type as those extracted during the training, passing the unlabeled feature samples to the machine learning model, and outputting from the machine learning model indicia of a gesture classified by the machine learning model. | 09-05-2013 |
20130238530 | SYSTEMS AND METHODS FOR GENERATING WIND POWER SCENARIOS FOR WIND-POWER-INTEGRATED STOCHASTIC UNIT COMMITMENT PROBLEMS - The present disclosure relates generally to systematic algorithms (and associated systems and methods) that take a forecast model as input and produce a discrete probability distribution as output, using scenario reduction ideas from stochastic programming. In one example, an algorithm (and associated system and method) creates scenarios sequentially for each time period, leading to a scenario tree. | 09-12-2013 |
20130238531 | Automatic Combination and Mapping of Text-Mining Services - Embodiments of systems and methods for automatic combination of text mining services, may comprise an instance generation component and an auto-mapping component. From common text sources, the instance generation component generates instances for taxonomy elements attached to a particular text mining service. These instances are then forwarded to the auto-mapping component, which computes a mapping between different taxonomies. This mapping may be saved to avoid repeated instance generation and mapping processes for similar taxonomies. The computed mapping may in turn be input to a result combiner element, together with extraction results from calling the different text mining services. The result combiner returns the merged result sets to the execution, and finally to the user or API. | 09-12-2013 |
20130238532 | Method and Apparatus for Identifying Structural Deformation - A method and apparatus for identifying deformation of a structure. Training deformation data is identified for each training case in a plurality of training cases. Training strain data is identified for each training case in the plurality of training cases. The training deformation data and the training strain data are configured for use by a heuristic model to increase an accuracy of output data generated by the heuristic model. A group of parameters for the heuristic model is adjusted using the training deformation data and the training strain data for the each training case in the plurality of training cases such that the heuristic model is trained to generate estimated deformation data for the structure based on input strain data. The estimated deformation data has a desired level of accuracy. | 09-12-2013 |
20130238533 | MACHINE LEARNING METHODS AND SYSTEMS FOR IDENTIFYING PATTERNS IN DATA - Methods for training machines to categorize data, and/or recognize patterns in data, and machines and systems so trained. More specifically, variations of the invention relates to methods for training machines that include providing one or more training data samples encompassing one or more data classes, identifying patterns in the one or more training data samples, providing one or more data samples representing one or more unknown classes of data, identifying patterns in the one or more of the data samples of unknown class(es), and predicting one or more classes to which the data samples of unknown class(es) belong by comparing patterns identified in said one or more data samples of unknown class with patterns identified in said one or more training data samples. Also provided are tools, systems, and devices, such as support vector machines (SVMs) and other methods and features, software implementing the methods and features, and computers or other processing devices incorporating and/or running the software, where the methods and features, software, and processors utilize specialized methods to analyze data. | 09-12-2013 |
20130238534 | METHOD AND SYSTEM FOR PREDICTION AND ROOT CAUSE RECOMMENDATIONS OF SERVICE ACCESS QUALITY OF EXPERIENCE ISSUES IN COMMUNICATION NETWORKS - Embodiments of the invention utilize advanced statistical data analytics to predict and provide recommendations for root-cause analysis for service access QoE issues in networks, such as 3G/4G networks. Using FCAPS data as predictor variables, embodiments are configured to set up the problem as a predictive regression or classification problem to estimate service access QoE related indicators. Some embodiments perform training and tuning of various non-linear statistical modelling algorithms, based for example on tree and ensemble methods, using network deregistration information from RAN logs. | 09-12-2013 |
20130238535 | ADAPTATION OF CONTEXT MODELS - There is disclosed a method including receiving sensor data extracted from one or more physical sensors, using the extracted sensor data and a context model to perform a first level context determination, and examining the at least one condition. If the examining indicated that the at least one condition was fulfilled, the context model is adapted on the basis of the sensor data, otherwise adaptation data formed on the basis of the sensor data is provided to a second level context determination. A corresponding apparatus and computer program product are also provided. | 09-12-2013 |
20130238536 | System and Method for Secure Addition of Machine Readable Identification to an Off-line Database - A system that allows secure identification tokens (e.g., smart cards or RFID tags), often used for enabling such systems, to be securely added to a secure, local database of identification tokens authorized to operate the device, system or service. Such authorizations may be open-ended or have an expiration date. The addition of the identification token is achieved without requiring communication with a central controller by wired or wireless means, but is instead triggered by a message authorizing the addition of an identification token to the local database. The same invention can be used, in some embodiments, to allow magnetic stripe cards or biometric measurements to become authorized to operate or allow access to the system or device or service. | 09-12-2013 |
20130246317 | SYSTEM, METHOD AND COMPUTER READABLE MEDIUM FOR IDENTIFYING THE LIKELIHOOD OF A STUDENT FAILING A PARTICULAR COURSE - Systems and methods for assessing the likelihood of a student of an educational institution failing a particular course taken by the student. Configuration data that identifies, for a particular course, a plurality of grade book applications, a plurality of student information systems, and a plurality of learning management systems is stored in the memory of a computing device. A processor receives, via adapters, input data from grade book applications, student information systems, and learning management systems. The adapters transform the input data into a standard risk model for the processor to generate, based on the received risk data, a signal indicative of a likelihood of a student failing a particular course. | 09-19-2013 |
20130246318 | INFORMATION PROCESSING APPARATUS, INFORMATION PROCESSING METHOD, AND PROGRAM - There is provided an information processing apparatus including a reward estimator generator using action history data, including state data expressing a state, action data expressing an action taken by an agent, and a reward value expressing a reward obtained as a result of the action, as learning data to generate, through machine learning, a reward estimator estimating a reward value from inputted state data and action data. The reward estimator generator includes: a basis function generator generating a plurality of basis functions; a feature amount vector calculator calculating feature amount vectors by inputting state data and action data in the action history data into the basis functions; and an estimation function calculator calculating an estimation function estimating the reward value included in the action history data from the feature amount vectors according to regressive/discriminative learning. The reward estimator includes the plurality of basis functions and the estimation function. | 09-19-2013 |
20130246319 | Projection Mining For Advanced Recommendation Systems And Data Mining - A method for projection mining comprises performing a first projection on a first data object of a first type comprising a plurality of data entries and a second data object of a second type comprising a plurality of data entries to create definitions of attributes of the first data object and definitions of attributes of the second data object, performing a second projection of the definitions of the attributes of the first data object and the definitions of the attributes of the second data object into a space of meta-attributes based on semantic relationships among the attributes of the first data object and the second data object, learning relationships between the space of meta-attributes formed by the projections of the first data object and the second data object and a space of meta-attributes relating to new data not included in the first data object and the second data object, and generating at least one new data object of the first or second type based on the new data using the learned relationships. | 09-19-2013 |
20130254140 | METHOD AND SYSTEM FOR ASSESSING AND UPDATING USER-PREFERENCE INFORMATION - Disclosed are a variety of methods and systems for processing access-only user-behavior data and developing and using user-preference models. In one example embodiment, a method for ascribing a score to a first portion of preference data includes establishing a model of user-preference data and receiving the first portion of preference data at a first computerized device and storing that data. The method further includes calculating at least one statistic in relation to the first portion of the preference data by way of a processing device of either the first computerized device or a second computerized device and performing at least one additional operation, by way of either the processing device or another processing device, by which the at least one statistic is evaluated in relation to the model, whereby as a result of being evaluated, the at least one statistic is converted into the score. | 09-26-2013 |
20130254141 | APPARATUS AND METHOD FOR ANALYSING EVENTS FROM SENSOR DATA BY OPTIMISATION - The present invention relates to sensor signal analysis. It relates particularly, but not exclusively, to methods, systems and devices for monitoring and processing the sensor signals to determine automatically characteristics of events represented by the sensor signals. The present invention is particularly, but not exclusively, related to methods, systems and devices for monitoring moisture in absorbent articles such as diapers, incontinence garments, dressings and pads resulting from wetness events caused by, for example, urinary and/or faecal incontinence. In an embodiment, the invention includes a method for processing sensor signals representing an event in an absorbent article. The method comprises: receiving sensor signals from a sensor representing one or more events in an absorbent article; and processing the sensor signals to determine a characteristic of at least one event in the absorbent article. One such characteristic can include the volume of a voiding event such as a urinary incontinence event. In another embodiment, the method includes carrying out a learning phase including the steps of: receiving sensor signals representing one or more events in each of one or more absorbent articles; receiving observation data indicative of a cumulative characteristic of the one or more events in each absorbent article; and identifying an optimal mathematical model describing a relationship between the sensor signals and the observation data. Such events can include urinary incontinence events occurring in absorbent articles such as diapers. Observation data can be measured cumulative volume of a cycle of voiding events occurring in a diaper. | 09-26-2013 |
20130254142 | DISTRIBUTED EVOLUTIONARY ALGORITHM FOR ASSET MANAGEMENT AND TRADING - A server computer and a multitude of client computers form a network computing system that is scalable and adapted to continue to evaluate the performance characteristics of a number of genes generated using a software application running on the client computers. Each client computer continues to periodically receive data associated with the genes stored in its memory. Using this data, the client computers evaluate the performance characteristic of their genes by comparing a solution provided by the gene with the periodically received data associated with that gene. Accordingly, the performance characteristic of each gene may be updated and varied with each periodically received data. The performance characteristic of a gene defines its fitness. The genes may be virtual asset traders that recommend trading options, and the data associated with the genes may be historical trading data. | 09-26-2013 |
20130254143 | ATTRIBUTE VALUE ESTIMATION DEVICE, ATTRIBUTE VALUE ESTIMATION METHOD, PROGRAM, AND RECORDING MEDIUM - The present invention provides an attribute value estimation device capable of yielding highly accurate estimation results even when people from multiple races are estimation targets. The attribute value estimation device for estimating, from data input thereto, an attribute value of the data includes: a data acquisition unit ( | 09-26-2013 |
20130262349 | COMPUTER-IMPLEMENTED SYSTEM WITH ADAPTIVE COGNITIVE FEATURES AND METHOD OF USING THE SAME - A computer-implemented system includes an edge module and at least one input device coupled to the edge module. The at least one input device is configured to generate data input signals. The system also includes a cognitive module coupled to the edge module. The cognitive module includes a perception sub-module coupled to the edge module. The perception sub-module is configured to receive the data input signals. The cognitive module also includes a learning sub-module coupled to the perception sub-module. The learning sub-module is configured to adaptively learn at least in part utilizing the data input signals. | 10-03-2013 |
20130262350 | LEARNING REWRITE RULES FOR SEARCH DATABASE SYSTEMS USING QUERY LOGS - Methods and arrangements for conducting a search using query logs. A query log is consulted and query rewrite rules are learned automatically based on data in the query log. The learning includes obtaining click-through data present in the query log. | 10-03-2013 |
20130262351 | LEARNING REWRITE RULES FOR SEARCH DATABASE SYSTEMS USING QUERY LOGS - Methods and arrangements for conducting a search using query logs. A query log is consulted and query rewrite rules are learned automatically based on data in the query log. The learning includes obtaining click-through data present in the query log. | 10-03-2013 |
20130262352 | APPARATUS AND METHOD FOR RECOGNIZING USER ACTIVITY - A user activity real-time recognition apparatus and method are provided and include a collector configured to collect a frequency-domain signal for each user activity and to generate learning data based on the frequency-domain signal. The apparatus and method also include an extractor configured to extract a user activity feature from the frequency-domain signal based on an activity feature extracting model. The activity feature extracting model is learned based on the learning data from the collector. The apparatus and method further include a classifier configured to analyze the user activity feature to classify a user activity pattern based on an activity pattern classifying model and configured to transmit the classified user activity pattern to an application device. | 10-03-2013 |
20130262353 | OPTIMAL ONLINE ADAPTIVE CONTROLLER - Various embodiments are disclosed for optimal online adaptive control. One such method includes a cost function determination by a critic network coupled to the system under control. The cost function is one produces a minimum value for a cost of the system under control when applied by an action network. The method also includes a control input determination by an action network. The control input determination uses the cost function to determine a control input to apply to the system under control. The control input is one that produces the minimum value for the cost of the system under control. The method also includes simultaneously tuning respective parameters of the critic network and the action network by applying respective tuning laws that do not involve the system dynamics function ƒ(x) for the system under test. | 10-03-2013 |
20130262354 | TERMINAL DEVICE, TERMINAL CONTROL METHOD, PROGRAM AND INFORMATION PROCESSING SYSTEM - A terminal device includes an acquisition unit which acquires local information of a present location at a present time; an accumulation unit which accumulates the acquired local information for a predetermined period; a communication unit which transmits the local information which is accumulated for a predetermined period to an information processing device, and receives parameters of a statistical model which are learned using the local information, which is acquired using the information processing device from a plurality of portable terminal devices, from the information processing device; and a prediction unit which predicts the local information in relation to an arbitrary time and location using the received parameters of the statistical model. | 10-03-2013 |
20130262355 | TOOLS AND METHODS FOR DETERMINING SEMANTIC RELATIONSHIP INDEXES - Systems, apparatuses, and methods for determining an individual's sentimental baseline, based on a plurality of data items and characteristics. The data items may include objective and quantitative data, as well as subjective and qualitative data. The system, apparatus, or method may obtain a number of relationships between information atoms, identify sentiments associated with the relationships, and calculate sentimental baselines for those relationships. Differences from any baseline may also be calculated, to determine true changes in sentiment. Relationships between those differences and other data or relationships may also be calculated, to determine how a change in sentiment is related to other changes in behavior. For example, relationships between a particular difference and changes in any metric, sub-metric, group of characteristics, data item, data source, characteristic, sentiment, or groups thereof may be determined. These relationships may also be used to predict future behavior or sentiment. | 10-03-2013 |
20130268465 | METHODS AND SYSTEMS FOR COMPUTER-BASED SELECTION OF IDENTIFYING INPUT FOR CLASS DIFFERENTIATION - In systems and methods for computer-based selection of identifying input for differentiating classes, training regions (each of which is associated with a defined class) are specified in a training space that is organized by data bands according to selected definitions. Windows are defined in training elements associated with data locations in the training regions. Multiple training windows are defined in the training elements in a known band in the training data. Relevance measures for training windows represent an extent of likelihood of correctly identifying class for a test location based on data band, window position within the training element, and the frequency of occurrence of data symbols in training windows at the window position. The window having the highest value relevance measure is selected as the most relevant window. Multiple most relevant windows, together with their parameters, are selected as identifying input to facilitate class differentiation in test spaces. | 10-10-2013 |
20130268466 | SYSTEM FOR PREDICTING LIFETIME OF BATTERY - A system for predicting a lifetime of a battery cell, including a learning data input unit, the learning data input unit being configured to receive at least one learning measurement factor and at least one learning factor, a target data input unit, the target data input unit being configured to receive at least one target factor, a machine learning unit, the machine learning unit being coupled to the learning data input unit, the machine learning unit assigning weights to respective ones of the learning factors input to the learning data input unit, and a lifetime prediction unit, the lifetime prediction unit being coupled to the target data input unit and the machine learning unit, the lifetime prediction unit using the weights assigned by the machine learning unit to predict one or more characteristics indicative of the lifetime of the target battery cell. | 10-10-2013 |
20130268467 | TRAINING FUNCTION GENERATING DEVICE, TRAINING FUNCTION GENERATING METHOD, AND FEATURE VECTOR CLASSIFYING METHOD USING THE SAME - Provided is a training function generating method. The method includes: receiving training vectors; calculating a training function from the training vectors; comparing a classification performance of the calculated training function with a predetermined classification performance and recalculating a training function on the basis of a comparison result, wherein the recalculating of the training function includes: changing a priority between a false alarm probability and a miss detection probability of the calculated training function; and recalculating a training function according to the changed priority. | 10-10-2013 |
20130268468 | METHOD AND APPARATUS FOR INTENT MODELING AND PREDICTION - A method and apparatus enables identification of customer characteristics and behavior, and predicts the customer's intent. Such prediction can be used to adopt various business strategies to increase the chances of conversion of customer interaction to a sale, and thereby can increase revenue, and/or enhance the customer's experience. | 10-10-2013 |
20130268469 | INCREASING SIGNAL TO NOISE RATIO FOR CREATION OF GENERALIZED AND ROBUST PREDICTION MODELS - A computer system iteratively executes a decision tree-based prediction model using a set of input variables. The iterations create corresponding rankings of the input variables. The computer system generates overall variables contribution data using the rankings of the input variables and identifies key input variables based on the overall variables contribution data. | 10-10-2013 |
20130275346 | INTELLIGENT SPECTRUM ALLOCATION BASED ON USER BEHAVIOR PATTERNS - A platform to facilitate transferring spectrum rights is provided that includes a database to ascertain information regarding available spectrum for use in wireless communications. A request for spectrum use from an entity needing spectrum may be matched with available spectrum. This matching comprises determining a pattern in user requests overtime to optimize spectrum allocation. The Cloud Spectrum Services (CSS) process allows entities to access spectrum they would otherwise not have; it allows the end user to complete their download during congested periods while maintaining high service quality; and it allows the holder of rental spectrum to receive compensation for an otherwise idle asset. | 10-17-2013 |
20130275347 | APPARATUS AND METHOD FOR PREDICTING POTENTIAL CHANGE OF CORONARY ARTERY CALCIFICATION (CAC) LEVEL - An apparatus and a method predict a patient's potential change of Coronary Artery Calcification (CAC) level using various risk factors including a Coronary Artery Calcification Score (CACS). The apparatus includes a receiving unit, a cluster determining unit, a risk factor score extracting unit, a prediction model storage unit, a prediction model learning unit, and a predicting unit, and the method includes a receiving process, a risk factor score extracting process, and an operation performing process. | 10-17-2013 |
20130275348 | SYSTEM AND METHODS FOR GENERATING OPTIMAL POST TIMES FOR SOCIAL NETWORKING SITES - A system and methods are disclosed for determining the ideal times for a person, software client, or other entity to post a message to a social networking site. An ideal time is a time when the post will have a maximum impact, where impact is some measure of success as defined by the posting entity. To determine the ideal times, the posting patterns of individual users are aggregated in a weighted fashion, taking into consideration both the entity's desired impact and the likelihood that the user will be online and able to view the post within a specified time frame. | 10-17-2013 |
20130275349 | Comprehensive Glaucoma Determination Method Utilizing Glaucoma Diagnosis Chip And Deformed Proteomics Cluster Analysis - Provided is a technique for determining a physiological attribute in a mammal, including the onset or progression of human glaucoma, with high accuracy. The results of the determination of genotype date and the results of the determination of cytokine date are consolidated by a consolidated determination unit ( | 10-17-2013 |
20130275350 | METHODS AND SYSTEMS FOR IDENTIFYING PATIENTS WITH MILD CONGNITIVE IMPAIRMENT AT RISK OF CONVERTING TO ALZHEIMER'S - Methods and systems for selecting a cohort group or a patient at risk from a population of patients with mild cognitive impairment. The methods include using a computer configured to perform the steps: receiving normalized learning data from a portion of the population of patients; tuning a set of decision trees on the normalized learning data; receiving patient data from one or more patients of the population, wherein the patient data is independent from the learning data; classifying the patient data with the tuned set of decision trees to obtain patient threshold values; and displaying the patient threshold values. | 10-17-2013 |
20130282627 | LEARNING MULTIPLE TASKS WITH BOOSTED DECISION TREES - A multi-task machine learning method is performed to generate a multi-task (MT) predictor for a set of tasks including at least two tasks. The machine learning method includes: learning a multi-task decision tree (MT-DT) including learning decision rules for nodes of the MT-DT that optimize an aggregate information gain (IG) that aggregates single-task IG values for tasks of the set of tasks; and constructing the MT predictor based on the learned MT-DT. In some embodiments the aggregate IG is the largest single-task IG value of the single-task IG values. In some embodiments the machine learning method includes repeating the MT-DT learning operation for different subsets of a training set to generate a set of learned MT-DT's, and the constructing comprises constructing the MT predictor as a weighted combination of outputs of the set of MT-DT's. | 10-24-2013 |
20130282628 | Method and Apparatus for Performing Dynamic Textual Complexity Analysis Using Machine Learning Artificial Intelligence - A data processing system including one or more client devices, wherein each client device is connected to a network system and a data center unit. The data center unit includes a network interface unit, a user interface, one or more storage devices, wherein the one or more storage devices comprise one or more databases. Further, the data center unit includes a storage device controller and database manager for controlling the operation of storage devices and databases, a web server for providing web services to clients, a database server for providing database services to the one or more clients and a machine learning artificial intelligence application server for predicting textual complexity of data. The machine learning artificial intelligence application server includes one or more databases for storing data used to refine textual complexity analysis for improved accuracy of textual complexity predictions. | 10-24-2013 |
20130282629 | PREDICTING TRANSITION FROM LAMINAR TO TURBULENT FLOW OVER A SURFACE USING MODE-SHAPE PARAMETERS - In accordance with embodiments disclosed herein, there are provided methods, systems, and apparatuses for predicting whether a point on a computer-generated aircraft or vehicle surface is adjacent to laminar or turbulent flow is made using a transition prediction technique. A plurality of boundary-layer properties at the point are obtained from a steady-state solution of a fluid flow in a region adjacent to the point. Included in the list of boundary-layer properties are computed coefficients or weights of mode shapes that describe the boundary-layer profiles. A plurality of instability modes are obtained, each defined by one or more mode parameters. A vector of regressor weights is obtained for the known instability growth rates in a training dataset. For each instability mode in the plurality of instability modes, a covariance vector is determined, which is the covariance of a predicted local growth rate with the known instability growth rates. Each covariance vector is used with the vector of regressor weights to determine a predicted local growth rate at the point. Based on the predicted local growth rates, an n-factor envelope at the point is determined. | 10-24-2013 |
20130282630 | Task-agnostic Integration of Human and Machine Intelligence - A system combines inputs from human processing and machine processing, and employs machine learning to improve processing of individual tasks based on comparison of human processing results. Once performance of a particular task by machine processing reaches a threshold, the level of human processing used on that task is reduced. | 10-24-2013 |
20130282631 | INFORMATION PROPAGATION PROBABILITY FOR A SOCIAL NETWORK - One or more techniques and/or systems are disclosed for predicting propagation of a message on a social network. A predictive model is trained to determine a probability of propagation of information on the social network using both positive and negative information propagation feedback, which may be collected while monitoring the social network over a desired period of time for information propagation. A particular message can be input to the predictive model, which can determine a probability of propagation of the message on the social network, such as how many connections may receive at least a portion of the message and/or a likelihood of at least a portion of the message reaching respective connections in the social network. | 10-24-2013 |
20130282632 | LINK SPAM DETECTION USING SMOOTH CLASSIFICATION FUNCTION - A spam detection system is disclosed. The system includes a classifier training component that receives a first set of training pages labeled as normal pages and a second set of training pages labeled as spam pages. The training component trains a web page classifier based on both the first set of training pages and the second set of training pages. A spam detector then receives unlabeled web pages uses the web page classifier to classify the unlabeled web pages as spam pages or normal pages. | 10-24-2013 |
20130290222 | RETRIEVAL SYSTEM AND METHOD LEVERAGING CATEGORY-LEVEL LABELS - An instance-level retrieval method and system are provided. A representation of a query image is embedded in a multi-dimensional space using a learned projection. The projection is learned using category-labeled training data to optimize a classification rate on the training data. The joint learning of the projection and the classifiers improves the computation of similarity/distance between images by embedding them in a subspace where the similarity computation outputs more accurate results. An input query image can thus be used to retrieve similar instances in a database by computing the comparison measure in the embedding space. | 10-31-2013 |
20130290223 | METHOD AND SYSTEM FOR DISTRIBUTED MACHINE LEARNING - Method, system, and programs for distributed machine learning on a cluster including a plurality of nodes are disclosed. A machine learning process is performed in each of the plurality of nodes based on a respective subset of training data to calculate a local parameter. The training data is partitioned over the plurality of nodes. A plurality of operation nodes are determined from the plurality of nodes based on a status of the machine learning process performed in each of the plurality of nodes. The plurality of operation nodes are connected to form a network topology. An aggregated parameter is generated by merging local parameters calculated in each of the plurality of operation nodes in accordance with the network topology. | 10-31-2013 |
20130290224 | System or Solution Index Fault - Assessment, Identification, Baseline, and Alarm Feature - A system may be assessed, based on support engineer knowledge, to identify specific, predictive, index fault indicators. The identified fault indicators may be fed into an embedded automation system on a network device, which is used to baseline the fault indicators, and then subsequently provide alerts when problems begin, so that corrective action may be taken. | 10-31-2013 |
20130290225 | SYSTEMS AND METHODS FOR SELECTING AND ANALYZING PARTICLES IN A BIOLOGICAL TISSUE - Systems and methods are disclosed for jointly presenting and analyzing morphological characteristics and biomarker expression levels of a biological sample. The systems and methods may utilize a morphological selection component to isolate a population of biological particles in a biological sample for exclusion from further processing. In addition, the systems and methods may simultaneously render morphological and statistical representations of the biological sample on a user interface. | 10-31-2013 |
20130290226 | SYSTEM AND METHOD FOR SOCIAL GRAPH AND GRAPH ASSETS VALUATION AND MONETIZATION - A system and method to provide social and graph credit scoring, valuation and monetization. The valuation and monetization system provides users, service providers and other agents with a credit scoring and rating system. An application programming interface provides a platform for integration of all types of financial, business and personal services into the logic and classification infrastructure. A graph assets and collateralization clearinghouse creates a secure platform for collateralization. The graph assets information database provides core classification services for ranking, indexing, and content analysis. In a specific embodiment a financial services provider utilizes the social credit scoring, valuation and monetization platform to process and approve qualified credit line applicants. Approval is primarily based on graph and valuation metrics provided by the system and includes an e-commerce and social metrics real time analysis for knowledge of an applicant's future and present risk profile, including credit and graph properties risks. | 10-31-2013 |
20130290227 | Systems and Methods to Facilitate Local Searches via Location Disambiguation - Systems and methods use machine learning techniques to resolve location ambiguity in search queries. In one aspect, a dataset generator generates a training dataset using query logs of a search engine. A training engine applies a machine learning technique to the training dataset to generate a location disambiguation model. A location disambiguation engine uses the location disambiguation model to resolve location ambiguity in subsequent search queries. | 10-31-2013 |
20130290228 | RECOGNITION DICTIONARY GENERATING DEVICE AND PATTERN RECOGNITION DEVICE - A recognition dictionary generating device includes a unit that acquires plural reference vectors each containing an offset value indicating a degree of importance; a unit that selects a first reference vector belonging to the class same as an input vector and having the minimum distance from the input vector, and a second reference vector belonging to a class different from the input vector and having the minimum distance from the input vector; a unit that acquires a first distance value indicating a distance between the input vector and the first reference vector and a second distance value indicating a distance between the input vector and the second reference vector; a unit that corrects the first reference vector and the second reference vector using a coefficient changing in accordance with a relationship between the first distance value and the second distance value, the first distance value, and the second distance value; and a determining unit that determines a reference vector to be excluded from a recognition dictionary in accordance with the offset value of the corrected first reference vector and second reference vector. | 10-31-2013 |
20130290229 | GRIPPING-FEATURE LEARNING AUTHENTICATION SYSTEM AND GRIPPING-FEATURE LEARNING AUTHENTICATION METHOD - A gripping-feature learning authentication system preventing impersonation. A mobile information terminal includes a trigger monitor outputting a gripping-feature acquisition signal, a gripping-feature sample acquisition part acquiring a gripping-feature sample, a template learning part learning a user authentication template, a template sending part sending the user authentication template, an authentication request part sending and receiving an authentication request, an authentication data sending part sending a terminal identification number or gripping-feature sample, and a determination result receiver receiving a determination result. A server includes a template receiver receiving a user authentication template, a member template storage storing a user authentication template and member information, an authentication data receiver receiving a terminal identification number and gripping-feature sample, a member authenticator determining probability users corresponding to terminal identification numbers and gripping-feature samples belong to same member group, and a determination result sending part sending a determination result. | 10-31-2013 |
20130297536 | MENTAL HEALTH DIGITAL BEHAVIOR MONITORING SUPPORT SYSTEM AND METHOD - A system and method for monitoring a user's mental health tor and collect data concerning. The user's use of electronic devices is tracked, such as usage of his mobile phone, tablet and his web activity. The invention “learns” each patient's unique behavioral patterns to be used as a “base line” representing the steady state (chronic phase) of the patient. The algorithmic processing unit detects any irregularities in a patient's behavioral patterns and produces a deterioration prediction. If it is determined that a threshold is exceeded, an alert is sent to a health professional. | 11-07-2013 |
20130297537 | Method and System for creating Dynamic Neural Function Libraries - The current invention comprises a function library and relates to Artificial Intelligence systems and devices. Within a Dynamic Neural Network (the “Intelligent Target Device”) training model values are autonomously generated in during learning and stored in synaptic registers. One instance of an Intelligent Target Device is the “Autonomous Learning Dynamic Artificial Neural Computing Device and Brain Inspired System”, described in patent application number 20100076916 and referenced in whole in this text. A collection of values that has been generated in synaptic registers comprises a training model, which is an abstract model of a task or a process that has been learned by the intelligent target device. A means is provided within the Intelligent Target Device to copy the training model to computer memory. A collection of such training model sets are stored within a function library on a computer storage facility, such as a disk, CD, DVD or other means. | 11-07-2013 |
20130304676 | ON-DEVICE REAL-TIME BEHAVIOR ANALYZER - Methods, systems and devices for generating data models in a communication system may include applying machine learning techniques to generate a first family of classifier models using a boosted decision tree to describe a corpus of behavior vectors. Such behavior vectors may be used to compute a weight value for one or more nodes of the boosted decision tree. Classifier models factors having a high probably of determining whether a mobile device behavior is benign or not benign based on the computed weight values may be identified. Computing weight values for boosted decision tree nodes may include computing an exclusive answer ratio for generated boosted decision tree nodes. The identified factors may be applied to the corpus of behavior vectors to generate a second family of classifier models identifying fewer factors and data points relevant for enabling the mobile device to determine whether a behavior is benign or not benign. | 11-14-2013 |
20130304677 | Architecture for Client-Cloud Behavior Analyzer - Methods, systems and devices for generating data models in a client-cloud communication system may include applying machine learning techniques to generate a first family of classifier models that describe a cloud corpus of behavior vectors. Such vectors may be analyzed to identify factors in the first family of classifier models that have the highest probably of enabling a mobile device to conclusively determine whether a mobile device behavior is malicious or benign. Based on this analysis, a a second family of classifier models may be generated that identify significantly fewer factors and data points as being relevant for enabling the mobile device to conclusively determine whether the mobile device behavior is malicious or benign based on the determined factors. A mobile device classifier module based on the second family of classifier models may be generated and made available for download by mobile devices, including devices contributing behavior vectors. | 11-14-2013 |
20130304678 | METHOD FOR PARTIAL LEARNING SHARING OF A SOFTWARE APPLICATION - A method for sharing, by a secondary machine ( | 11-14-2013 |
20130311406 | Automated Object Classification Using Temperature Profiles - Methods and apparatus are provided for automated object classification using temperature profiles. An object in an environment (such as an exemplary data center) is classified by obtaining a surface temperature profile of the object; and classifying the object as a particular type of equipment based on the obtained surface temperature profile. The surface temperature profile of the object can be compared to a plurality of predefined characteristic surface temperature profiles each associated with a given type of equipment. | 11-21-2013 |
20130311407 | Automated Object Classification Using Temperature Profiles - Methods and apparatus are provided for automated object classification using temperature profiles. An object in an environment (such as an exemplary data center) is classified by obtaining a surface temperature profile of the object; and classifying the object as a particular type of equipment based on the obtained surface temperature profile. The surface temperature profile of the object can be compared to a plurality of predefined characteristic surface temperature profiles each associated with a given type of equipment. | 11-21-2013 |
20130311408 | Determining and Predicting Popularity of Content - Processes and systems are described herein that may be used to predict which content (e.g., programs, series, movies, channels etc.) will be popular in the future. The processes and systems may use a model that is trained using historical data reflecting information about past showings of programs, such as rating information, viewer behaviors (e.g., channel changes and DVR recordings), online social activity (e.g., Facebook likes and relevant Twitter messages), and/or other data. Accordingly, it may be possible to provide predictive recommendations of popular content before, for example, the content is scheduled or otherwise planned to be distributed or made available to viewers. The results of such prediction may be integrated with, for example, a program guide available to viewers. | 11-21-2013 |
20130311409 | Web-Based Education System - A web-based education system enables instructors to prepare and present online education courses and enables students to locate and participate in available courses. A machine learning algorithm generates a topic-based representation of courses and generates a topic-based representation of user interests. The web-education system then enables users to find relevant courses using the topic-based representations. Recommended courses are ranked according to factors such as relevance to the user, popularity, and course rating. | 11-21-2013 |
20130311410 | Information Processing Apparatus, Information Processing Method, and Program - An information processing apparatus for generating a similarity determination algorithm determining a similarity between a pair of data. The apparatus includes: a feature-quantity-extraction expression list generation mechanism generating a feature quantity-extraction expression list including a plurality of feature-quantity-extraction expressions including a plurality of operators by updating the feature-quantity extraction expression list of a preceding generation; a calculation mechanism inputting first and second data given as teacher data into each of the feature-quantity-extraction expressions in the feature-quantity-extraction expression list to calculate a feature quantity corresponding to each of the first and the second data; an evaluation-value calculation mechanism calculating the evaluation value of each of the feature-quantity-extraction expressions using the calculated feature quantities and a similarity between the first and the second data; and a similarity-calculation expression estimation mechanism estimating a similarity calculation expression for calculating a similarity between the first and the second data. | 11-21-2013 |
20130318011 | Method for Detecting Anomalies in Multivariate Time Series Data - A method detects anomalies in time series data, wherein the time series data is multivariate, by partitioning time series training data into partitions. A representation for each partition in each time window is determined to form a model of the time series training data, wherein the model includes representations of distributions of the time series training data. The representations obtained from partitions of time series test data are compared to the model to obtain anomaly scores. | 11-28-2013 |
20130318012 | Processing of Information - Systems and methods are provided for deriving a prediction from existing data by utilizing information extraction and machine learning, wherein both approaches can be optimized independently from each other. Optionally, deductive reasoning may also be combined with information extraction and machine learning and may as well be optimized independently from the other two functionalities. The two or three functionalities may utilize at least one set of data and may (at least partially) process various sets of data. The combined approach may produce significantly improved results, and may be implemented in various technical fields, applications and use cases involving, e.g., data mining or processing of huge amounts of data. The disclosed systems and methods may be applicable for all kinds of technical systems, e.g., medical, genetic research, or industry and automation systems. | 11-28-2013 |
20130318013 | INFORMATION PROCESSING APPARATUS, INFORMATION PROCESSING METHOD, AND PROGRAM - There is provided an information processing apparatus including an evaluation giving unit that gives an evaluation based on a second user to an item in a list created on a basis of a first user, and a display control unit that controls display of the list, on a basis of the evaluation based on the second user. | 11-28-2013 |
20130318014 | INTERACTIONS AMONG ONLINE DIGITAL IDENTITIES - A computer-implemented method is provided, which includes constructing, by a computer system, first and second user profiles for respective first and second users, by observing respective online behaviors of the first and second users, respectively. The computer system identifies that the first and second users have similar interests. Responsively to the identifying, the computer system transfers information to the first user profile from the second user profile, thereby modifying the first user profile. Content is presented to the first user responsively to the modified first user profile. | 11-28-2013 |
20130318015 | NETWORK DATA MINING TO DETERMINE USER INTEREST - Mining information from network data traffic to determine interests of online network users is provided herein. A data packet received at a network interface device can be accessed and inspected at line rate speeds. Source or addressing information in the data packet can be extracted to identify an initiating and/or receiving device. The packet can be inspected to identify occurrences of keywords or data features related with one or more subject matters. A vector can be defined for a network device that indicates a relative rank of interest in various subject matters. Furthermore, statistical analysis can be implemented on data stored in one or more interest vectors to determine information pertinent to network user interests. The information can facilitate providing value-added products or services to network users. | 11-28-2013 |
20130325755 | METHODS AND SYSTEMS FOR OPTIMIZING MESSAGES TO USERS OF A SOCIAL NETWORK - Techniques to optimize messages sent to a user of a social networking system. In one embodiment, information about the user may be collected by the social networking system. The information may be applied to train a model for determining likelihood of a desired action by the user in response to candidate messages that may be provided for the user. The social networking system may provide to the user a message from the candidate messages with a selected likelihood of causing the desired action. | 12-05-2013 |
20130325756 | GRAPH-BASED FRAMEWORK FOR MULTI-TASK MULTI-VIEW LEARNING - A system and method a Multi-Task Multi-View (M | 12-05-2013 |
20130325757 | CASCADING LEARNING SYSTEM AS SEMANTIC SEARCH - A cascading learning system as a semantic search is described. The cascading learning system has a request analyzer, a request dispatcher and classifier, a search module, a terminology manager, and a cluster manager. The request analyzer receives a request for search terms from a client application and determines term context in the request to normalize request data from the term context. The normalized request data are classified and dispatched to a corresponding domain-specific module. Each domain-specific module of a search module generates a prediction with a trained probability of an expected output. The terminology manager receives normalized request data from the request dispatcher and classifier, and manages terminology stored in a contextual network. The cluster manager controls data flow between the request dispatcher and classifier, the search module container, the terminology manager, and a business data source system. | 12-05-2013 |
20130325758 | TAILORED OPERATING SYSTEM LEARNING EXPERIENCE - This document describes techniques and apparatuses enabling a tailored operating system learning experience. The techniques can tailor a learning experience to a user's computing device or a user's specifications. This tailoring to the user's computing device may include an interactive demonstration showing a new feature controlled through a mouse if the user's computing device has a mouse, or a touchscreen if the user's computing device has a touchscreen, for example. Further, this tailoring may include showing a new feature according to a user's specifications, such as describing a feature using a large font or with a large, bright mouse-pointer if the user indicated that he or she is visually impaired. | 12-05-2013 |
20130325759 | METHODS AND APPARATUS FOR PERFORMING TRANSFORMATION TECHNIQUES FOR DATA CLUSTERING AND/OR CLASSIFICATION - Some aspects include transforming data for which at least one constraint has been specified on a portion of the data, the at least one constraint relating to a similarity and/or dissimilarity of at least some of the portion of the data. Techniques comprise determining a first transformation that approximates the at least one constraint using a cosine similarity as a measure of the similarity and/or dissimilarity of the at least a portion of the data, applying at least the first transformation to the data to obtain transformed data, and fitting a plurality of clusters to the transformed data to obtain a plurality of established clusters. Some aspects include classifying input data by transforming the input data using at least the first transformation and comparing the transformed input data to the established clusters. | 12-05-2013 |
20130325760 | METHOD FOR EXTRACTING CRITICAL DIMENSION OF SEMICONDUCTOR NANOSTRUCTURE - A method for extracting a critical dimension of a semiconductor nanostructure. The method includes: 1) determining a value range for each parameter to be extracted, whereby generating an electronic spectra database, and employing training spectra and support vector machine (SVM) training networks for training of SVMs; 2) employing the SVMs after training to map measured spectra to yield a corresponding electronic spectra database; and 3) employing a searching algorithm to search for an optimum simulation spectrum in the corresponding electronic spectra database, simulation parameters corresponding to the simulation spectrum being the critical dimension of the semiconductor nanostructure to be extracted. | 12-05-2013 |
20130325761 | METHOD AND APPARATUS FOR DETECTING ABNORMAL TRANSITION PATTERN - A method for detecting an abnormal transition pattern from a transition pattern includes: first extracting an episode pattern with an appearance frequency greater than or equal to a first frequency from an episode pattern represented with a description form so as to include a first transition pattern and a second transition pattern differing in an order of a part of items from the first transition pattern to have a complementary relation thereto; second extracting a third transition pattern with an appearance frequency greater than or equal to a second frequency from the transition pattern; and specifying a transition pattern other than the third transition pattern from transition patterns included in the extracted episode pattern, and determining an abnormal transition pattern based on the transition pattern specified in the specifying when the third transition pattern includes a fourth transition pattern corresponding to the extracted episode pattern in the first extracting. | 12-05-2013 |
20130325762 | ADAPTIVE REMOTE MAINTENANCE OF ROLLING STOCKS - Adaptive remote maintenance of rolling stocks is provided by machine-learning ( | 12-05-2013 |
20130325763 | PREDICTING LIKELIHOOD OF ON-TIME PRODUCT DELIVERY, DIAGNOSING ISSUES THAT THREATEN DELIVERY, AND EXPLORATION OF LIKELY OUTCOME OF DIFFERENT SOLUTIONS - A task effort estimator may determine a probability distribution of an estimated effort needed to complete unfinished tasks in a project based on one or more of a set of completed tasks belonging to a project and attributes associated with the completed tasks belonging to the project, a set of completed tasks not belonging to the project and attributes associated with the completed tasks not belonging to the project, or the combination of both. A project completion predictor may determine a probability distribution of completion time for the project based on the probability distribution of an estimated effort needed to complete the unfinished tasks in the project, and one or more resource and scheduling constraints associated with the project. | 12-05-2013 |
20130332398 | MONITORING AND REPLAYING USER BEHAVIORS ON THE WEB - The present disclosure is directed to methods and systems for monitoring and replaying user interactions with one or more interactive electronic documents. The methods generally include identifying an event comprising an interaction between a user and an interactive electronic document, determining to record the event, identifying for the event a user action, a target element, and a set of element features, and recording data for recreating the event. Generally, the methods and systems monitor a training user's interactions with a document and generate an automated replay agent capable of replaying or recreating those interactions on the document or on similar documents. In some embodiments, the replay agent is able to place a document in a desired state and extract information from the document in the desired state. In some embodiments, the replay agent is trained to recognize elements, or types of elements, in the document. | 12-12-2013 |
20130332399 | IDENTIFYING LIKELY FAULTY COMPONENTS IN A DISTRIBUTED SYSTEM - In general, techniques are described for automatically identifying likely faulty components in massively distributed complex systems. In some examples, snapshots of component parameters are automatically repeatedly fed to a pre-trained classifier and the classifier indicates whether each received snapshot is likely to belong to a fault and failure class or to a non-fault/failure class. Components whose snapshots indicate a high likelihood of fault or failure are investigated, restarted or taken off line as a pre-emptive measure. The techniques may be applied in a massively distributed complex system such as a data center. | 12-12-2013 |
20130332400 | SYSTEMS AND METHODS FOR RECOGNIZING AMBIGUITY IN METADATA - A method for estimating artist ambiguity in a dataset is performed at a device with a processor and memory storing instructions for execution by the processor. The method includes applying a statistical classifier to a first dataset including a plurality of media items, wherein each media item is associated with one of a plurality of artist identifiers, each artist identifier identifies a real world artist, and the statistical classifier calculates a respective probability that each respective artist identifier is associated with media items from two or more different real world artists based on a respective feature vector corresponding to the respective artist identifier. The method further includes providing a report of the first dataset, including the calculated probabilities, to a user of the electronic device. Each respective feature vector includes a plurality of features that indicate likelihood of artist ambiguity. | 12-12-2013 |
20130339276 | MULTI-TIERED APPROACH TO E-MAIL PRIORITIZATION - A method of automating incoming message prioritization. The method including training a global classifier of a computer system using training data. Dynamically training a user-specific classifier of the computer system based on a plurality of feedback instances. Inferring a topic of the incoming message received by the computer system based on a topic-based user model. Computing a plurality of contextual features of the incoming message. Determining a priority classification strategy for assigning a priority level to the incoming message based on the computed contextual features of the incoming message and a weighted combination of the global classifier and the user specific classifier. Classifying the incoming message based on the priority classification strategy. | 12-19-2013 |
20130339277 | Action-Oriented analytics-driven electronic data identification and labeling - A corporate compliance/document retrieval system and method for enabling automated software inspection of textual documents based upon seeding the software with examples of categories of interest. The system enables resultant actions such as breach dismissals, breach escalations, closer inspection of an offender's communications, and iterative machine learning when specific content is detected that is representative of a category of interest. The alerting breaches occur in near real time and can alleviate further breaches from occurring. | 12-19-2013 |
20130339278 | DATA DISCRIMINATION DEVICE, METHOD, AND PROGRAM - A data discrimination device is provided with: an estimating means that estimates the population structure of inputted learning data; a degree-of-fit calculating means that calculates the degree of fit, of each of the inputted addition candidate data, to the population of learning data, using results of the estimation by the estimating means; and a determining means for determining, on the basis of the calculated degree of fit, whether or not to add each of the addition candidate data to the learning data. | 12-19-2013 |
20130346346 | SEMI-SUPERVISED RANDOM DECISION FORESTS FOR MACHINE LEARNING - Semi-supervised random decision forests for machine learning are described, for example, for interactive image segmentation, medical image analysis, and many other applications. In examples, a random decision forest comprising a plurality of hierarchical data structures is trained using both unlabeled and labeled observations. In examples, a training objective is used which seeks to cluster the observations based on the labels and similarity of the observations. In an example, a transducer assigns labels to the unlabeled observations on the basis of the clusters and certainty information. In an example, an inducer forms a generic clustering function by counting examples of class labels at leaves of the trees in the forest. In an example, an active learning module identifies regions in a feature space from which the observations are drawn using the clusters and certainty information; new observations from the identified regions are used to train the random decision forest. | 12-26-2013 |
20130346347 | Method to Predict a Communicative Action that is Most Likely to be Executed Given a Context - Disclosed are apparatus and methods for providing machine-learning services. A context-identification system executing on a mobile platform can receive data comprising context-related data associated with the mobile platform and application-related data received from the mobile platform. The context-identification system can identify a context using the context-related data associated with the mobile platform and/or the application-related data received from the mobile platform. Based on at least one context identified, context-identification system can predict a communicative action associated with the mobile platform by performing a machine-learning operation on the received data. An instruction can be received to execute the communicative action associated with the mobile platform. | 12-26-2013 |
20130346348 | VISION-GUIDED ROBOTS AND METHODS OF TRAINING THEM - Via intuitive interactions with a user, robots may be trained to perform tasks such as visually detecting and identifying physical objects and/or manipulating objects. In some embodiments, training is facilitated by the robot's simulation of task-execution using augmented-reality techniques. | 12-26-2013 |
20130346349 | TEMPORAL DOCUMENT TRAINER AND METHOD - An electronic document sorter is trained to classify documents based on their temporal qualities. The invention can be used in environments such as automated news aggregators, search engines and other electronic systems which compile information having temporal qualities. | 12-26-2013 |
20140006318 | COLLECTING, DISCOVERING, AND/OR SHARING MEDIA OBJECTS | 01-02-2014 |
20140006319 | EXTENSION TO THE EXPERT CONVERSATION BUILDER | 01-02-2014 |
20140006320 | Method and system for robot understanding, knowledge, conversation, volition, planning, and actuation | 01-02-2014 |
20140006321 | METHOD FOR IMPROVING AN AUTOCORRECTOR USING AUTO-DIFFERENTIATION | 01-02-2014 |
20140012785 | APPARATUS AND METHOD FOR RECOGNIZING REPRESENTATIVE USER BEHAVIOR BASED ON RECOGNITION OF UNIT BEHAVIORS - An apparatus for recognizing a representative user behavior includes a unit-data extracting unit configured to extract at least one unit data from sensor data, a feature-information extracting unit configured to extract feature information from each of the at least one unit data, a unit-behavior recognizing unit configured to recognize a respective unit behavior for each of the at least one unit data based on the feature information, and a representative-behavior recognizing unit configured to recognize at least one representative behavior based on the respective unit behavior recognized for each of the at least one unit data. | 01-09-2014 |
20140012786 | COMPENSATION DATA PREDICTION - A method of predicting compensation data includes obtaining compensation data, associated with a job category, with at least one datum being associated with each of a plurality of characteristics associated with the job category, determining values of factors, associated with respective ones of the characteristics, and a base value that when used as operands of a function yield estimates of the obtained data such that relationships between the estimates and corresponding obtained compensation data satisfy at least one criterion, and using a portion of the values of factors and the base value by a computer to automatically obtain estimates of compensation data. | 01-09-2014 |
20140012787 | SYSTEM AND METHOD FOR INFORMATION PROCESSING AND MOTOR CONTROL - The present invention relates to a system and method for information process and motor control using artificially constructed apparatus. More specially, the present invention provides a system and method that can process nature language and other informational input including visual, audio and other sensory inputs and respond intelligently. | 01-09-2014 |
20140019386 | Computerized Logical Decision and Learning System - In various embodiments, a computer-implemented method for decision making and learning is disclosed. The method comprises receiving, by a processor, an alert indicating a change of a monitored system. The method further comprises selecting, by the processor, a statistically best action for responding to the alert and initiating, by the processor, the statistically best action. | 01-16-2014 |
20140019387 | MACHINE LEARNING FOR DATABASE MIGRATION SOURCE - Technologies are generally provided for maintaining performance level of a database being migrated between different cloud-based service providers employing machine learning. In some examples, data requests submitted to an original data store/database may be submitted to a machine learning-based filter for recording and analysis. Based on the results of the data requests and the filter analyses, new key value structures for a new data store/database may be created. The filter may assign performance scores to the original data requests (made to the original data store) and data requests made to the newly-created key value structures. The filter may then compare the performance scores associated with the created key value structures to each other and to performance scores associated with the original data requests and may select the created key value structures with performance scores that are at least substantially equal to those of the original data requests for the new data store. | 01-16-2014 |
20140019388 | SYSTEM AND METHOD FOR LOW-RANK MATRIX FACTORIZATION FOR DEEP BELIEF NETWORK TRAINING WITH HIGH-DIMENSIONAL OUTPUT TARGETS - Systems and methods for reducing a number of training parameters in a deep belief network (DBN) are provided. A method for reducing a number of training parameters in a deep belief network (DBN) comprises determining a network architecture including a plurality of layers, using matrix factorization to represent a weight matrix of a final layer of the plurality of layers as a plurality of matrices, and training the DBN having the plurality of matrices. | 01-16-2014 |
20140019389 | Method, Software, and System for Making a Decision - A method for making a decision includes receiving a first decision question from a first user, providing a database of information regarding elements of decision quality, populating the database with data corresponding to each element of decision quality as applied to the first decision question, and providing a decision recommendation for the first decision question based at least in part on the data. | 01-16-2014 |
20140019390 | APPARATUS AND METHOD FOR AUDIO FINGERPRINTING - Apparatus and method for fingerprinting an audio signal utilizes programmed machine to identify overlapping windows in a time domain representation of the audio signal, establish a frequency domain representation of the overlapping windows, convolve a set of two-dimensional kernels with the frequency domain representation to thereby provide a convolutional layer as an output stage, reduce dimensionality of the convolution layer to provide one or more further output stages, and perform further processing so as to output a decision in regard to a plurality of the overlapping windows comprising either a specific content id that matches to the audio signal or a failure-to-match indication. | 01-16-2014 |
20140025606 | METHODS FOR SOLVING COMPUTATIONAL PROBLEMS USING A QUANTUM PROCESSOR - Methods for solving a computational problem including minimizing an objective including a set of weights and a dictionary by casting the weights as Boolean variables and alternately using a quantum processor and a non-quantum processor to successively optimize the weights and the dictionary, respectively. A first set of values for the dictionary is guessed and the objective is mapped to a QUBO. A quantum processor is used to optimize the objective for the Boolean weights based on the first set of values for the dictionary by minimizing the resulting QUBO. A non-quantum processor is used to optimize the objective for the dictionary based on the Boolean weights by updating at least some of the columns of the dictionary. These processes are successively repeated until a solution criterion is met. Minimization of the objective may be used to generate features in a learning problem and/or in data compression. | 01-23-2014 |
20140025607 | Confidence Based Vein Image Recognition and Authentication - An indexed hierarchical tree search structure converts each registration sample into an equivalent registration model based on the clustering of its registration item descriptors in the leaf nodes of the hierarchical tree. Query item descriptors from a query sample from someone wanting to be recognized are distributed into the hierarchical tree. A query model is defined based on the clustering of query item descriptors at the leaf nodes, and registration and verification are made based on comparison of the query model and the registration models. | 01-23-2014 |
20140025608 | System and Method for Generating Legal Documents - A system and method for the automated generation of documents for a legal transaction over a network using probabilistic prediction of customary usage. The predictions are generated by user experience, expert rules and machine learned classifiers based on user input of transaction data. The classifiers are constructed and tested on a partitioned dataset consisting of transaction data and legal document and clause selections in previous transactions. In one embodiment, such dataset is collected in a document management system. | 01-23-2014 |
20140025609 | Methods and Arrangements For Creating Customized Recommendations - A method and arrangement for creation of a customized recommendation of items in a user device ( | 01-23-2014 |
20140032448 | METHOD, COMPUTER PROGRAMS AND A USE FOR THE PREDICTION OF THE SOCIOECONOMIC LEVEL OF A REGION - The method includes a computing mechanism running in a computer device receiving as inputs, the geographical region R, base stations giving coverage to the geographical region R and call records generated by individuals using the base stations. Prediction of the socioeconomic level is automatically performed by using information during a given time period from the call records. The computer programs include code adapted for computing the average socioeconomic value for each coverage region and computing a set of variables when the program is run on a computer. | 01-30-2014 |
20140032449 | Automated Remediation with an Appliance - In one embodiment, a method includes receiving information associated with the operation of one or more network devices, indexing the information for analysis, analyzing the information to determine a pattern in the information, generating one or more labels for at least a portion of the information based at least in part on the pattern, and making the information and labels available to a remediation system. | 01-30-2014 |
20140032450 | CLASSIFYING UNCLASSIFIED SAMPLES - A system and method for classifying unclassified samples. The method includes detecting a number of classes including training samples in training data sets. The method includes, for each class, determining a vector for each training sample based on a specified number of nearest neighbor distances between the training sample and neighbor training samples, and determining a class distribution based on the vectors. The method also includes detecting an unclassified sample in a data set and, for each class, determining a vector for the unclassified sample based on the specified number of nearest neighbor distances between the unclassified sample and nearest neighbor training samples within the class, and determining a probability that the unclassified sample is a member of the class based on the vector and the class distribution. The method further includes classifying the unclassified sample based on the probabilities. | 01-30-2014 |
20140032451 | SUPPORT VECTOR MACHINE-BASED METHOD FOR ANALYSIS OF SPECTRAL DATA - Support vector machines are used to classify data contained within a structured dataset such as a plurality of signals generated by a spectral analyzer. The signals are pre-processed to ensure alignment of peaks across the spectra. Similarity measures are constructed to provide a basis for comparison of pairs of samples of the signal. A support vector machine is trained to discriminate between different classes of the samples. to identify the most predictive features within the spectra. In a preferred embodiment feature selection is performed to reduce the number of features that must be considered. | 01-30-2014 |
20140032452 | RECOMMENDATION AGENT USING A PERSONALITY MODEL DETERMINED FROM MOBILE DEVICE DATA - A user's context history is analyzed to build a personality model describing the user's personality and interests. The personality model includes a plurality of metrics indicating the user's position on a plurality of personality dimensions, such as desire for novelty, tendency for extravagance, willingness to travel, love of the outdoors, preference for physical activity, and desire for solitude. A customized recommendation agent is then built based on the personality model, which selects a recommendation from a corpus to present to the user based on an affinity between the user's personality and the selected recommendation. | 01-30-2014 |
20140032453 | CONTEXTUAL INFORMATION PROVIDER - A user's context history is used to help select contextual information to provide to the user. Context data describing the user's current context is received and a plurality of information items corresponding to the user's current context are identified from a contextual information corpus. A personalized user behavior model for the user is applied to determine the likelihood that each of the identified information items will be of value to the user. One or more of the information items are selected based on the corresponding likelihoods and the selected information items are provided for presentation to the user. | 01-30-2014 |
20140040169 | ACTIVE LEARNING WITH PER-CASE SYMMETRICAL IMPORTANCE SCORES - A method for classifying cases includes receiving a pool of unlabeled cases with associated per-case symmetrical importance scores, applying a selection algorithm with a classifier to a training set and the pool, but without the per-case symmetrical importance scores, to determine selection scores for the unlabeled case, and combining the selection scores and the corresponding per-case symmetrical importance scores to form combined scores for the unlabeled cases. The method further includes providing a high scoring unlabeled case to an oracle to label, receiving a labeled case back from the oracle and augmenting the training set with the labeled case, training the classifier with the augmented training set, and applying the classifier to an additional unlabeled case. | 02-06-2014 |
20140040170 | SYSTEM AND METHOD FOR IDENTIFYING ABUSIVE ACCOUNT REGISTRATION - Disclosed is a system and method for processing account registration by identifying account candidates attempting to open an account as abusive. That is, the present disclosure discusses identifying, and challenging and marking abusive account registration. The present disclosure takes into account users' behaviors on a network and the impact to the cost and/or revenue of the network. The present disclosure is proactive as it allows for actions to be taken at the earliest possible time in the registration process before an account is created. This prevents abusive activity from taking place within the network and effecting services and privileges available to legitimate users. Additionally, the effects of the disclosed systems and methods minimize the negative impacts of abusive activity on normal user accounts. | 02-06-2014 |
20140040171 | CONTENT-BASED DEMOGRAPHIC ESTIMATION OF USERS OF MOBILE DEVICES AND USAGE THEREOF - Method, apparatus and product for content-based demographic estimation of users of mobile devices and usage thereof. One method comprising: obtaining a list of applications that are installed on a mobile device; and estimating, based on the list of applications, one or more demographic parameter of a user of the mobile device. Another method, that is performed by a mobile device, comprising: obtaining a list of applications that are installed on said mobile device, wherein based on the list of applications, one or more demographic parameters of a user of said mobile device are determined; and performing a user engagement based on the estimated one or more demographic parameters. | 02-06-2014 |
20140040172 | Privacy-Preserving Aggregated Data Mining - An apparatus, system and method are introduced for preserving privacy of data in a dataset in a database with a number n of entries. In one embodiment, the apparatus includes memory including computer program code configured to, with a processor, cause the apparatus to form a random matrix of dimension m by n, wherein m is less than n, operate on the dataset with the random matrix to produce a compressed dataset, form a pseudoinverse of the random matrix, and operate on the dataset with the pseudoinverse of the random matrix to produce a decompressed dataset. | 02-06-2014 |
20140040173 | SYSTEM AND METHOD FOR DETECTION OF A CHARACTERISTIC IN SAMPLES OF A SAMPLE SET - A computer-implemented method for detecting a characteristic in a sample of a set of samples is described. The method may include receiving from a user an indication for each sample of said set of samples that the user determines to include the characteristic. The method may also include defining samples of said set of samples that were not indicated by the user to include the characteristic as not including the characteristic. The method may further include iteratively applying by a processing unit, a detection algorithm on a first subset of the set of samples, said detection algorithm using a set of detection criteria that includes one or a plurality of detection criteria, evaluating a detection performance of the detection algorithm and modifying the detection algorithm by making changes in the set of detection criteria to enhance detection performance of the learning algorithm. The method may still further include, upon reaching a desired level of detection performance for the modified detection algorithm, performing validation by testing the modified detection algorithm on a second subset of the set of samples. | 02-06-2014 |
20140040174 | ANOMALY DETECTION FOR CLOUD MONITORING - Technologies are presented for anomaly detection in a cloud environment using a sparsity measure. In some examples, cloud metric data may be gathered and processed into a dictionary base. Linear transform coefficients for a test sample may then be calculated from the dictionary base using l | 02-06-2014 |
20140040175 | LOCATION DETERMINATION USING GENERALIZED FINGERPRINTING - An RF fingerprinting methodology is generalized to include non-RF related factors. For each fingerprinted tile, there is an associated distance function between two fingerprints (the training fingerprint and the test fingerprint) from within that tile which may be a linear or non-linear combination of the deltas between multiple factors of the two fingerprints. The distance function for each tile is derived from a training dataset corresponding to that specific tile, and optimized to minimize the total difference between real distances and predicted distances. Upon receipt of an inference request, a result is derived from a combination of the fingerprints from the training dataset having the least distance per application of the distance function. Likely error for the tile is also determined to ascertain whether to rely on other location methods. | 02-06-2014 |
20140046877 | SYSTEM AND METHOD FOR BUILDING RELATIONSHIP HIERARCHY - The various embodiments herein provide a method and system for building a relationship hierarchy from big data. The method comprises extracting a plurality of relationships defined between entities from a big data, building relationship recognition models adapted to identify different forms of generic relationships, resolving the relationships by grouping the similar relationships together and separating the relationships which are syntactically and semantically dissimilar and reconciling the resolved relationships to build the relationship hierarchy. The relationship hierarchy comprises groups and subgroups of relationships created based on generic relationship similarity based on a contextual aspect and a specialization aspect using a Language and Domain model. The method of extraction of plurality of relationships from the unstructured data is a self-learning process which uses open information extraction techniques for learning new relationships. | 02-13-2014 |
20140046878 | METHOD AND SYSTEM FOR DETECTING SOUND EVENTS IN A GIVEN ENVIRONMENT - A method and system for detecting abnormal events in a given environment comprises a model construction step comprising: a) a step of unsupervised initialization of Q groups; b) a step of definition of a model of normality consisting of 1-class SVM classifiers; c) a step of optimum distribution of the audio signals in the Q different groups; d) repetition of the steps b and c until a stop criterion C | 02-13-2014 |
20140046879 | MACHINE LEARNING SEMANTIC MODEL - The subject technology discloses configurations for creating reusable predictive models for applying to one or more data sources. The subject technology specifies a business problem to determine a probability of an event occurring. The business problem may include a constraint. A data source is selected for a predictive model associated with a predictive algorithm in which the predictive model includes one or more queries and parameters. A set of transformations are then determined based on the queries and parameters for at least a subset of data from the data source to be processed by the predictive algorithm. The subject technology identifies a set of patterns based on the set of transformations for at least the subset of data from the data source. A trained predictive model is then provided including the determined set of patterns, the set of transformations, and the associated predictive algorithm for solving the specified business problem. | 02-13-2014 |
20140046880 | Dynamic Predictive Modeling Platform - Methods, systems, and apparatus, including computer programs encoded on one or more computer storage devices, for training and retraining predictive models. A series of training data sets are received and added to a training data queue. In response to a first condition being satisfied, multiple retrained predictive models are generated using the training data queue, multiple updateable trained predictive models obtained from a repository of trained predictive models, and multiple training functions. In response to a second condition being satisfied, multiple new trained predictive models are generated using the training data queue, at least some training data stored in a training data repository and training functions. The new trained predictive models include static trained predictive models and updateable trained predictive models. The repository of trained predictive models is updated with at least some of the retrained predictive models and new trained predictive models. | 02-13-2014 |
20140052673 | SYSTEM, METHOD AND COMPUTER PROGRAM PRODUCT FOR CLASSIFICATION OF SOCIAL STREAMS - A method of labeling an unlabeled message of a social stream. The method including training a training model based on labeled messages, partitioning the training model into a plurality of class partitions, each comprising statistical information and a class label, computing a confidence for each of the class partitions based on information of an unlabeled message and the statistical information of a respective class partition, as executed by a processor in a computer system, and labeling the unlabeled message of the social stream according to respective confidences of the class partitions. | 02-20-2014 |
20140052674 | SYSTEM, METHOD AND COMPUTER PROGRAM PRODUCT FOR CLASSIFICATION OF SOCIAL STREAMS - A system that labels an unlabeled message of a social stream. The system including a memory device storing instructions to execute a training model, the training model being trained based on labeled messages, and partitioned into a plurality of class partitions, each of which comprise statistical information and a class label, and a Central Processing Unit (CPU) that computes a confidence for each of the class partitions based on information of an unlabeled message and the statistical information of a respective class partition, and that labels the unlabeled message according to respective confidences of the class partitions. | 02-20-2014 |
20140052675 | SCHEDULE MANAGEMENT METHOD, SCHEDULE MANAGEMENT SERVER, AND MOBILE TERMINAL USING THE METHOD - A schedule management method is provided. The schedule management method includes acquiring user schedule information, extracting at least one piece of expected event information based on the user schedule information, collecting event information generated through near field communication, comparing the collected event information and the expected event information, and providing guidance information corresponding to the collected event information based on a result of the comparison. | 02-20-2014 |
20140058982 | AUDIO BASED CONTROL OF EQUIPMENT AND SYSTEMS - A method for controlling a device responsive to an audio signal captured using an audio sensor. A data processor is used to automatically analyze the audio signal using a plurality of semantic concept detectors to determine corresponding preliminary semantic concept detection values, each semantic concept detector being adapted to detect a particular semantic concept. The preliminary semantic concept detection values are analyzed using a joint likelihood model based on predetermined pair-wise likelihoods that particular pairs of semantic concepts co-occur to determine updated semantic concept detection values. One or more semantic concepts are determined based on the updated semantic concept detection values, and the device is controlled responsive to identified semantic concepts. The semantic concept detectors and the joint likelihood model are trained together with a joint training process using training audio signals, at least some of which are known to be associated with a plurality of semantic concepts. | 02-27-2014 |
20140058983 | SYSTEMS AND METHODS FOR TRAINING AND CLASSIFYING DATA - A mechanism for training and classifying data is disclosed. The method includes receiving a data set having at least a first annotation and at least a second annotation. The first annotation and the second annotation represent characteristics within the data set. The method also includes determining a first identifier from the first annotation and a second identifier from the second annotation and associating the first identifier to the second identifier to generate a joined identifier. The method also includes computing feature weights and transition weights for the annotated data set based on the at least a first identifier, at least a second identifier, and at least a joined identifier and transitions between each of the first, the second and the joined identifiers. The method further includes receiving a second un-annotated data set and classifying the second data set based on the computed feature weights and the transition weights. | 02-27-2014 |
20140058984 | INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING METHOD, PROGRAM FOR INFORMATION PROCESSING DEVICE, AND RECORDING MEDIUM - A base word to be a base, a compound word in which the base word becomes a modifiee, classification items to classify the compound word, and feature information about a feature that provides a clue to classify the compound word are acquired (S | 02-27-2014 |
20140058985 | Method and Device for Synthesis of Network Traffic - Embodiments of the present invention provide a method and device for synthesis of network traffic. The method includes extracting a first real traffic composition parameter sequence and a second real traffic composition parameter sequence from real traffic. A first synthetic traffic composition parameter sequence is generated. Relational coefficients between first traffic composition parameters and second traffic composition parameters are obtained. A second synthetic traffic composition parameter sequence is generated and synthetic traffic is generated according to the first synthetic traffic composition parameter sequence and the second synthetic traffic composition parameter sequence. | 02-27-2014 |
20140058986 | Enhanced DeepQA in a Medical Environment - A DeepQA engine is enhanced to provide a digital medical investigation tool which assists a medical professional in researching potential causes of a set of patient conditions, including clues, facts and factoids about the patient. The DeepQA engine provides one or more answers to a natural language question with confidence levels for each answer. If a confidence level falls below a threshold, the enhanced DeepQA engine performs a crowd sourcing operation to gather additional information from one or more domain experts. The domain expert responses are provided to the medical professional, and are learned by the enhanced DeepQA system to provide for better research of similar patient conditions in future queries. | 02-27-2014 |
20140067728 | SCALABLE STRING MATCHING AS A COMPONENT FOR UNSUPERVISED LEARNING IN SEMANTIC META-MODEL DEVELOPMENT - A string analysis tool for calculating a similarity metric between a source string and a plurality of target strings. The string analysis tool may include optimizations that may reduce the number of calculations to be carried out when calculating the similarity metric for large volumes of data. In this regard, the string analysis tool may represent strings as features. As such, analysis may be performed relative to features (e.g., of either the source string or plurality of target strings) such that features from the strings may be eliminated from consideration when identifying target strings for which a similarity metric is to be calculated. The elimination of features may be based on a minimum similarity metric threshold, wherein features that are incapable of contributing to a similarity metric above the minimum similarity metric threshold are eliminated from consideration. | 03-06-2014 |
20140067729 | Human Memory Enhancement Using Machine Learning - Techniques for providing a prompt for real-time cognitive assistance. A method includes analyzing input from at least one environmental sensor to identify context information pertaining to a user situation, identifying a likely subsequent cognitive task of the user in the user situation based on the context information and use of a learned model, determining an action with respect to information to be suggested to the user via a corresponding prompt, wherein the determining is based on the likely subsequent cognitive task, the context information and information learned from at least one previous user situation, computing a confidence value to represent a level of certainty in the action, and providing the prompt to the user if the action has a confidence value greater than a threshold value corresponding to the action. | 03-06-2014 |
20140067730 | Human Memory Enhancement Using Machine Learning - A system and an article of manufacture for providing a prompt for real-time cognitive assistance include analyzing input from at least one environmental sensor to identify context information pertaining to a user situation, identifying a likely subsequent cognitive task of the user in the user situation based on the context information and use of a learned model, determining an action with respect to information to be suggested to the user via a corresponding prompt, wherein the determining is based on the likely subsequent cognitive task, the context information and information learned from at least to one previous user situation, computing a confidence value to represent a level of certainty in the action, and providing the prompt to the user if the action has a confidence value greater than a threshold value corresponding to the action. | 03-06-2014 |
20140067731 | MULTI-DIMENSIONAL INFORMATION ENTRY PREDICTION - Intended meanings of user input character strings having multiple interpretations and having a single intended meaning are predicted in real time as they are entered by applying a profile thereto. The profile is generated by application of unsupervised and supervised learning techniques which identify predictive factors. Substantially simultaneously with application of the profile to the character strings, a plurality of remotely located profiles are accessed. The remotely located profiles are determined by application of supervised and unsupervised techniques to a plurality of third parties and are identified and ranked to determine those profiles that most nearly match one or more predictive factors in the user profile, where the predictive factors are weighted based on the ranking, the weighted predictive factors are combined with the user profile to determine the intended meaning of the character string from among the multiple interpretations, and the intended meaning is displayed to the user. | 03-06-2014 |
20140067732 | TRAINING DECISION SUPPORT SYSTEMS FROM BUSINESS PROCESS EXECUTION TRACES THAT CONTAIN REPEATED TASKS - A method for training a machine learning tool to generate a prediction in a business process includes receiving a business process model corresponding to the business process, the business process model including a plurality of tasks, identifying a cycling set at a decision point in the business process model, wherein the cycling set comprises at least one task that the business process model iterates through, and building a training table by determining a total number of sub-traces and a total number of variables from a plurality of execution traces of the business process model based on the cycling set identified at the decision point, wherein a new row of the training table is created for each of the sub-traces and a new column of the training table is created for each of the variables. | 03-06-2014 |
20140067733 | EXPERT SYSTEM FOR PREDICTION OF CHANGES TO LOCAL ENVIRONMENT - Disclosed is a photometer that employs high dynamic range (HDR) image processing and manipulation algorithms for capturing and measuring real-time sky conditions for processing into control input signals to a building's automated fenestration (AF) system, daylight harvesting (DH) system and HVAC system. The photometer comprises a color camera and a fitted fish-eye lens to capture 360-degree, hemispherical, low dynamic range (LDR) color images of the sky. Both camera and lens are housed in a sealed enclosure protecting them from environmental elements and conditions. In some embodiments the camera and processes are controlled and implemented by a back-end computer. | 03-06-2014 |
20140067734 | ANOMALY DETECTION IN SPATIAL AND TEMPORAL MEMORY SYSTEM - Detecting patterns and sequences associated with an anomaly in predictions made a predictive system. The predictive system makes predictions by learning spatial patterns and temporal sequences in an input data that change over time. As the input data is received, the predictive system generates a series of predictions based on the input data. Each prediction is compared with corresponding actual value or state. If the prediction does not match or deviates significantly from the actual value or state, an anomaly is identified for further analysis. A corresponding state or a series of states of the predictive system before or at the time of prediction are associated with the anomaly and stored. The anomaly can be detected by monitoring whether the predictive system is placed in the state or states that is the same or similar to the stored state or states. | 03-06-2014 |
20140074758 | SELF ORGANIZING MAPS FOR VISUALIZING AN OBJECTIVE SPACE - A method of visualizing a plurality of designs which comply with a plurality of objectives. The method comprises acquiring a plurality of designs each represented by sequential multivariate data indicative of a compliance with a plurality of objectives, generating an objective anchored based self-organizing map (SOM) having a plurality of objective anchors and maps the plurality of designs in an objective space, and outputting the objective anchored based SOM. Each objective anchor is associated with one of the plurality of objectives, each the design is visualized in the objective anchored based SOM by an indicator which the distance thereof from each the objective anchors is indicative of a compliance thereof with a respective the associated objective in relation to other of the plurality of objectives. | 03-13-2014 |
20140074759 | Identifying a Thumbnail Image to Represent a Video - Techniques are shown for generating image frames from a media presentation, selecting candidate thumbnails from the generated image frames using a selection process, and testing each selected candidate thumbnail for a success ranking relative to a target metric. The probability of choosing a selected candidate thumbnail with a success ranking higher than all other selected thumbnails as an optimum thumbnail for presentation to a user is based, at least in part, on the ratio of the success ranking of the selected candidate thumbnail with the highest success ranking to the sum of the success rankings of all of the selected candidate thumbnails. This Abstract is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. | 03-13-2014 |
20140074760 | METHOD AND APPARATUS FOR PROVIDING STANDARD DATA PROCESSING MODEL THROUGH MACHINE LEARNING - An approach for providing a standard data processing model through machine learning is described. A machine learning data processing platform may process and/or facilitate a processing of the at least one data set associated with one or more computation closures to determine at least one data pattern. The machine learning data processing platform may also determine one or more data processing models associated with the one or more computation closures, the at least one data set, or a combination thereof. The machine learning data processing platform may further cause, at least in part, a training of the one or more data processing models to reflect the at least one data pattern. | 03-13-2014 |
20140089234 | INTERACTIVE VISUALIZATION OF MULTI-OBJECTIVE OPTIMIZATION - A method for interactive visualization of multi-objective optimization is described. The method includes displaying a visualization of an approximation to a Pareto frontier for a multi-objective problem in a user interface. The method also includes updating the visualization of the approximation in real-time in response to finding a solution to the multi-objective problem while the multi-objective problem is being optimized. | 03-27-2014 |
20140089235 | ESTIMATING THE TIME UNTIL A REPLY EMAIL WILL BE RECEIVED USING A RECIPIENT BEHAVIOR MODEL - A system is provided. The system includes a recipient behavior model for generating an estimate of a receipt time of a reply email from a recipient of an initial email by applying machine learning to the initial email and to training data from other emails. The system further includes an indicator device for indicating the estimate to a user. | 03-27-2014 |
20140089236 | LEARNING METHOD USING EXTRACTED DATA FEATURE AND APPARATUS THEREOF - Disclosed is a learning method using extracted data features for simplifying a learning process or improving accuracy of estimation. The learning method includes dividing input learning data into two groups based on a predetermined reference, extracting data features for distinguishing the two divided groups, and performing learning using the extracted data features. | 03-27-2014 |
20140089237 | METHODS AND SYSTEMS FOR SCALABLE GROUP DETECTION FROM MULTIPLE DATA STREAMS - A system, method and computer program product for identifying strong links and discovering hidden relationships among entities, including identifying strong links and discovering hidden relationships among entities, wherein the entities include places, time slots, people, groups, and organizations; and identifying the strong links and discovering the hidden relationships based on low-level data streams, and incomplete and noisy evidence data streams. | 03-27-2014 |
20140089238 | INFORMATION PROCESSING DEVICE AND INFORMATION PROCESSING METHOD - Disclosed if an information processing device including a display control unit that controls a display of an input screen capable of inputting evaluations of an item based on a plurality of viewpoints, in accordance with a plurality of axes representing different viewpoints; an evaluation acquisition unit that acquires the evaluations of the item which are input by a user by the use of the input screen; and a transmission control unit that controls transmission of the evaluations of the item to another information processing device. | 03-27-2014 |
20140089239 | Methods, Apparatuses and Computer Program Products for Providing Topic Model with Wording Preferences - An apparatus for determining one more preferred words of a user may include a processor and memory storing executable computer program code that cause the apparatus to at least perform operations including implementing a topic model including data associated with one or more word preferences of at least one user. The computer program code may further cause the apparatus to implement a training model of the topic model to generate the word preferences based in part on analyzing training data of the training model. The training data including content associated with one or more determined topics. The computer program code may further cause the apparatus to determine that the word preferences correspond to one or more preferred words of respective users. Corresponding methods and computer program products are also provided. | 03-27-2014 |
20140095411 | ESTABLISHING "IS A" RELATIONSHIPS FOR A TAXONOMY - Disclosed are methods for returning to a user an answer to the question “what is .” Concepts and classes to which the concepts belong are determined from a corpus, such as taxonomy. The concepts are mapped to categories according to the structure of the taxonomy. Homonyms for words are collected and scored according to likeliness of use. Concept vectors are assembled for the identified concepts based on articles in the corpus and social media usage. Words are evaluated for generic-ness and a generic score is associated therewith. In responding to a query, the generic-ness of the terms of the query is evaluated and additional context solicited if the terms are generic. Candidate homonym concepts for a string in the query are selected according to context vectors for the homonym concepts. One or more homonym concepts are selected and the one or more categories corresponding to these concepts are returned. | 04-03-2014 |
20140095412 | SYSTEMS AND METHODS FOR EVENT TRACKING USING TIME-WINDOWED COUNTERS - To allow for tracking events and classifying assets within a social networking system. A time series of occurrences of an event type associated with at least one asset is generated. A first signal value and a second signal value is determined based on the time series. The at least one asset is classified based on comparison of the first signal value and the second signal value. In an embodiment, the time series is based on at least one time window including time intervals. In an embodiment, counters to determine a number of occurrences of an event type are associated with the time intervals. In an embodiment, each of the counters are incremented upon occurrence of the event type associated with the at least one asset during an associated time interval. | 04-03-2014 |
20140095413 | Associating a Web Session with a Household Member - A method for associating a web session with a particular member of a group of users includes: receiving a plurality of training web sessions, each training web session including one or more web events generated by a respective known user having one or more demographic attributes; training one or more binary classifiers using the training web sessions and the demographic attributes of the users; receiving a plurality of target web sessions, each target web session including one or more web events that are generated by a respective unknown member of a group of users, wherein each user has one or more demographic attributes; and applying one or more of the binary classifiers to the target web sessions such that a respective target web session is uniquely associated with a member based on, at least in part, the demographic attributes of the member. | 04-03-2014 |
20140095414 | METHOD AND APPARATUS TO GENERATE PLATFORM CORRECTABLE TX-RX - A programmable link training and status state machine is disclosed. The programmable finite state machine includes extra states, or shadow states, which are strategically used to debug a system design or to accommodate unexpected behavior, such as when the specifications of the design change. The programmable finite state machine is thus a mechanism to design in correctable logic, enabling the logic to be corrected in silicon and used in succeeding iterations of a product line. | 04-03-2014 |
20140095415 | APPARATUS AND METHOD FOR FORECASTING ENERGY CONSUMPTION - An apparatus for forecasting energy consumption includes a load data collection unit to collect low level data related to energy load data. The apparatus includes a filtering/attribute selection unit to eliminate duplicated attributes from attributes of low level data to produce an optimal attribute set. The apparatus includes a training unit produces a multi-class in which a plurality of single classes is hierarchically coupled in at least two levels and creates training data used for forecasting the energy consumption based on the produced multi-class. The apparatus includes a forecasting unit calculates the energy consumption to be forecasted on a basis of the real-time low level data, the multi-class and the training data. Therefore, it is possible to contribute to the progressive expansion and update of a cooling load forecasting system. | 04-03-2014 |
20140095416 | Mental Modeling Method and System - A mental modeling method and system may include providing at least one expert model, the at least one expert model including an analytical framework that summarizes subject matter expert-level knowledge. At least one mental model of at least one individual that summarizes subject matter individual-level knowledge is provided. The at least one expert model is modified based on the at least one mental model to provide at least one updated expert model. | 04-03-2014 |
20140095417 | SDI (SDI FOR EPI-DEMICS) - A computer system is adapted to predict the likelihood, temporal (or developmental) state, possible location(s), rate of spread or “infectiousness”, etc. of a potential epidemic. A wide and diverse range of inputs and associated parameters are inputted into the system some of which may be statistically correlatable with certain hidden states including those which are temporally oriented disease stages of progression as well as other types of attributes. A Dynamic Bayesian Belief Network or other adaptive or machine learning method is used for the probabilistic analysis. The system statistically analyzes and reanalyzes the totality of all recently updated information (and within the context of all past information), as can efficiently be modeled by the Dynamic Bayesian Belief Network or other adaptive or machine learning method to provide updated predictions and to suggest a recommended reactive protocol to an epidemic. | 04-03-2014 |
20140101079 | Massively Distributed Problem Solving Agent - A knowledge processing system that guides massive numbers of human agents and artificial agents to define, explore, and develop solutions for complex, problematic situations, that facilitates a seven-step process that proceeds from instance initiation, problem definition, problem exploration, approach selection, solution selection, to a time-sequenced action plan, and enables agents to subsequently modify process outputs and the action plan; a hybrid facilitation system in which machine and human agents jointly select the content and order of process steps and the system prompts that guide the problem-solving process; and an information-overload mitigation system which reduces the amount of information agents are required to process by means of natural language processing and graphical processing algorithms that characterize agent inputs and background materials, and identify, tag, sequester, and eliminate identical content and direct useful content to agent-defined points of application in the process. | 04-10-2014 |
20140101080 | APPARATUS AND METHOD OF DIAGNOSIS USING DIAGNOSTIC MODELS - An apparatus and a method for diagnosis are provided. The apparatus for diagnosis lesion include: a model generation unit configured to categorize learning data into one or more categories and to generate one or more categorized diagnostic models based on the categorized learning data, a model selection unit configured to select one or more diagnostic model for diagnosing a lesion from the categorized diagnostic models, and a diagnosis unit configured to diagnose the lesion based on image data of the lesion and the selected one or more diagnostic model. | 04-10-2014 |
20140101081 | SENTIMENT CLASSIFICATION USING OUT OF DOMAIN DATA - Providing sentiment classification of out of domain data are disclosed herein. In some aspects, a source domain having a trained classifier is matched to a target domain having a target classifier. The trained classifier may include identifiers that may be used to predict the sentiment of opinion data for the source domain. The target classifier may use the identifiers of the trained classifier to determine the sentiment of opinion data for the target domain. | 04-10-2014 |
20140101082 | AUTOMATED PRESENCE DETECTION AND PRESENCE-RELATED CONTROL WITHIN AN INTELLIGENT CONTROLLER - The current application is directed to intelligent controllers that use sensor output and electronically stored information, including one or more of electronically stored rules, parameters, and instructions, to determine whether or not one or more types of entities are present within an area, volume, or environment monitored by the intelligent controllers. The intelligent controllers select operational modes and modify control schedules with respect to the presence and absence of the one or more entities. The intelligent controllers employ feedback information to continuously adjust the electronically stored parameters and rules in order to minimize the number of incorrect inferences with respect to the presence or absence of the one or more entities and in order to maximize the efficiency by which various types of systems controlled by the intelligent controllers carry out selected operational modes. | 04-10-2014 |
20140101083 | SENSOR DATA PROCESSING - A method and apparatus for processing data, the data including: a set of one or more system inputs; and a set of one or more system outputs; wherein each system output corresponds to a respective system input; each system input includes a plurality of data points, a first data point in the plurality and a second data point in the plurality being from a same raw data source, and the first data point being pre-processed using a different pre-processing method relative to a pre-processing method used to pre-process the second data point, the method including: for each of the first and second data points, inferring a value indicative of a significance of the pre-processing method used to pre-process that data point; wherein the inferring includes performing a machine learning algorithm on a given system input from the data and a further system input. | 04-10-2014 |
20140108304 | Semantic Request Normalizer - A cascading learning system as a normalized semantic search is described. The cascading learning system has a request analyzer, a request dispatcher and classifier, a search module, a terminology manager, and a cluster manager. The request analyzer receives a request for search terms from a client application. The request analyzer has a normalization manager, a semantic parser, and a context builder. The normalization manager normalizes the search terms and generates a normalized semantic request based on a context. The request dispatcher and classifier classifies and dispatches the normalized semantic request to a corresponding domain-specific module that generates a prediction with a trained probability of an expected output. The terminology manager receives the normalized semantic request from the request dispatcher and classifier, and manages terminology stored in a contextual network. | 04-17-2014 |
20140108305 | RANKING FOR INDUCTIVE SYNTHESIS OF STRING TRANSFORMATIONS - Ranking technique embodiments are presented that use statistical and machine learning techniques to learn the desired ranking function for use in inductive program synthesis for the domain of string transformations. This generally involves automatically creating a training dataset of positive and negative examples from a given set of training tasks, each including multiple input-output examples. From the training dataset, a ranking function is learned that assigns an expression in a program in the domain specific language to a likelihood measure. This ranking function is then used to compute likelihoods of learnt programs from a very small number of input-output examples for a new task. | 04-17-2014 |
20140108306 | METHOD FOR TEACHING AN AFTERMARKET ACCESSORY COMPONENT, AND AN AFTERMARKET ACCESSORY COMPONENT CONFIGURED TO LEARN - A method for teaching an aftermarket accessory component how to actuate a vehicle function is disclosed herein. The aftermarket accessory component is configured to monitor communications across a vehicle bus. The method includes, but is not limited to, sampling message traffic transmitted across the vehicle bus while the vehicle function is not actuated. The method further includes setting filters in the aftermarket accessory component based on the sampled message traffic. The method further includes prompting a user to actuate the vehicle function in a first manner. The method further includes collecting filtered message traffic from the vehicle bus while the vehicle function is actuated in the first manner. The method further includes parsing the filtered message traffic to identify a command associated with actuation of the vehicle function. The method further includes testing the command to confirm that the command actuates the vehicle function. | 04-17-2014 |
20140108307 | METHODS AND SYSTEMS FOR PROVIDING PERSONALIZED AND CONTEXT-AWARE SUGGESTIONS - Embodiments of the disclosure relate to methods and systems for providing personalized and context-aware suggestions to a user. The method includes providing a user profile. Further, the method includes establishing contextual information regarding the user. Thereafter, one or more suggestions are provided to the user based on the user profile and the contextual information. Subsequently, the user profile based on the user feedback in response to the suggestion is modified. The user profile may be modified using a machine learning algorithm executed on a processor in order to improve the quality of the personalized and context-aware suggestions. In certain embodiments, the personalized and context-aware suggestions can be provided while the user is in a vehicle or while the user is operating a vehicle. | 04-17-2014 |
20140108308 | SYSTEM AND METHOD FOR COMBINING DATA FOR IDENTIFYING COMPATIBILITY - A method and system for combining data for identifying compatibility, having the steps of accessing at least one data source to extract data from the at least one data source that substantially merges all user data, classifying the data using a classification system, generating a data vector for the data, storing the data vector in the classification system, assessing a user attribute vector to the user data, comparing the data vector and the user attribute vector to produce at least one relationship recommendation, and providing to the user the at least one relationship recommendation. | 04-17-2014 |
20140108309 | Training a predictor of emotional response based on explicit voting on content and eye tracking to verify attention - Utilizing eye tracking to collect naturally expressed affective responses for training an emotional response predictor, comprising: receiving a vote of a user on a segment of content consumed by the user; receiving eye tracking data of the user taken while the user consumed the segment of content; determining, based on the eye tracking data, that a gaze-based attention level to the segment reaches a predetermined threshold; utilizing the vote to generate a label related to an emotional response to the segment; receiving an affective response measurement of the user taken substantially while the user consumed the segment of content; and training a measurement emotional response predictor with the label and the affective response measurement. | 04-17-2014 |
20140108310 | SYSTEM AND METHOD FOR OPTIMIZING TEAMS - A system, method and program product for optimizing a team to solve a problem. The system includes: a team building system for building a fundamental analytic team from a database of analysts to solve an inputted problem, wherein the fundamental analytic team includes at least one cluster of analysts characterized with specificity and at least one cluster of analysts characterized with sensitivity; and a problem analysis system that collects sensor data from the fundamental analytic team operating within an immersive environment, wherein the problem analysis system includes a system for evaluating the sensor data to identify a bias condition from the fundamental analytic team, and includes a system for altering variables in the immersive environment in response to a detected bias condition. | 04-17-2014 |
20140108311 | INFORMATION PORCESSING APPARATUS AND METHOD, AND PROGRAM THEREOF - There is provided an information processing apparatus including: evaluation information extracting means extracting evaluation information from evaluation of every user for an item; preference information creating means for creating preference information indicating a preference of every user on the basis of the evaluation information extracted by the evaluation information extracting means and an item characteristic amount indicating a characteristic of the item; space creating means for creating a space in which the user is located, according to the preference information; and display control means for controlling display of the user located in the space, according to the space created by the space creating means and the preference information. The apparatus may be applied to, for example, an image display apparatus which displays server images for providing a variety of items and information. | 04-17-2014 |
20140108312 | Computer-Implemented System And Method For Generating A Training Set For Use During Document Review - A computer-implemented system and method for generating a training set for use during document review is provided. Classification codes are assigned to a set of documents. Further classification codes are assigned to the same set of documents. The classification code for at least one document is compared with the further classification code for that document. A determination regarding whether a disagreement exists between the assigned classification code and the further classification code for at least one document is made. Those documents with disagreeing classification codes are identified as training set candidates. A stop threshold is applied to the training set candidates and the training set candidates are grouped as a training set when the stop threshold is satisfied. | 04-17-2014 |
20140114885 | URBAN TRAFFIC STATE DETECTION BASED ON SUPPORT VECTOR MACHINE AND MULTILAYER PERCEPTRON - A system and method that facilitates urban traffic state detection based on support vector machine (SVM) and multilayer perceptron (MLP) classifiers is provided. Moreover, the SVM and MLP classifiers are fused into a cascaded two-tier classifier that improves the accuracy of the traffic state classification. To further improve the accuracy, the cascaded two-tier classifier (e.g., MLP-SVM), a single SVM classifier and a single MLP classifier are fused to determine a final decision for a traffic state. In addition, fusion strategies are employed during training and implementation phases to compensate for data acquisition and classification errors caused by noise and/or outliers. | 04-24-2014 |
20140114886 | SYSTEM AND METHOD FOR AN INTERACTIVE QUERY UTILIZING A SIMULATED PERSONALITY - A system and method provides for an interactive query comprising a first input module capable of receiving input for creating a simulated personality for a first user. An expert system is capable of creating and storing the simulated personality. An output module is used for presenting the simulated personality to a second user. An interactive query module is capable of allowing the second user to communicate with the simulated personality of the first user. | 04-24-2014 |
20140114887 | HIGH PERFORMANCE INTERCONNECT PHYSICAL LAYER - A set of training sequences is generated, each training sequence to include a respective training sequence header, and the training sequence header is to be DC-balanced over the set of training sequences. The set of training sequences can be combined with electric ordered sets to form supersequences for use in such tasks as link adaptation, link state transitions, byte lock, deskew, and other tasks. | 04-24-2014 |
20140114888 | INFORMATION PROCESSING APPARATUS, INFORMATION PROCESSING METHOD, AND PROGRAM - There is provided an information processing apparatus including a manipulation model learning unit configured to learn a manipulation model regarding manipulation of a first object by a second object, by use of an actual image that is an actually observed image including the first object and the second object, the manipulation model associating a position and a change in state of the second object, when a state of the second object changes at a position in an object reference coordinate system with the first object regarded as a reference, with a change in state of the first object caused by the change in state of the second object. | 04-24-2014 |
20140114889 | METHOD AND SYSTEM FOR ASSESSMENT OF COGNITIVE FUNCTION BASED ON MOBILE DEVICE USAGE - A system and method that enables a person to unobtrusively assess their cognitive function from mobile device usage. The method records on the mobile device the occurrence and timing of user events comprising the opening and closing of applications resident on the device, the characters inputted, touch-screen gestures made, and voice inputs used on those applications, performs the step of learning a function mapping from the mobile device recordings to measurements of cognitive function that uses a loss function to determine relevant features in the recording, identifies a set of optimal weights that produce a minimum of the loss function, creates a function mapping using the optimal weights, and performs the step of applying the learned function mapping to a new recording on the mobile device to compute new cognitive function values. | 04-24-2014 |
20140114890 | PROBABILITY MODEL ESTIMATION DEVICE, METHOD, AND RECORDING MEDIUM - In order to learn an appropriate probability model in a probability model learning problem where a first issue and a second issue manifest concurrently by solving the two at the same time, provided is a probability model estimation device for obtaining a probability model estimation result from first to T-th (T≧2) training data and test data. The probability model estimation device includes: first to T-th training data distribution estimation processing units for obtaining first to T-th training data marginal distributions with respect to the first to the T-th training models, respectively; a test data distribution estimation processing unit for obtaining a test data marginal distribution with respect to the test data; first to T-th density ratio calculation processing units for calculating first to T-th density ratios, which are ratios of the test data marginal distribution to the first to the T-th training data marginal distributions, respectively; an objective function generation processing unit for generating an objective function that is used to estimate a probability model from the first to the T-th density ratios; and a probability model estimation processing unit for estimating the probability model by minimizing the objective function. | 04-24-2014 |
20140114891 | DISTRIBUTED SCALABLE INCREMENTALLY UPDATED MODELS IN DECISIONING SYSTEMS - In one embodiment, first weight information indicating a first set of delta values is obtained, where the first set of delta values includes a first delta value for each weight in a set of weights, the set of weights including a weight for each of a set of one or more parameters of a model. In addition, second weight information indicating a second set of delta values is obtained, where the second set of delta values includes a second delta value for each weight in the set of weights. Combined weight information including a combined set of delta values or a combined set of weights is generated based, at least in part, upon the first weight information and the second weight information. | 04-24-2014 |
20140122381 | DECISION TREE TRAINING IN MACHINE LEARNING - Improved decision tree training in machine learning is described, for example, for automated classification of body organs in medical images or for detection of body joint positions in depth images. In various embodiments, improved estimates of uncertainty are used when training random decision forests for machine learning tasks in order to give improved accuracy of predictions and fewer errors. In examples, bias corrected estimates of entropy or Gini index are used or non-parametric estimates of differential entropy. In examples, resulting trained random decision forests are better able to perform classification or regression tasks for a variety of applications without undue increase in computational load. | 05-01-2014 |
20140122382 | BAYESIAN MODELING OF PRE-TRANSPLANT VARIABLES ACCURATELY PREDICTS KIDNEY GRAFT SURVIVAL - An embodiment of the invention provides a method for determining a patient-specific probability of renal transplant survival. The method collects clinical parameters from a plurality of renal transplant donor and patient to create a training database. A fully unsupervised Bayesian Belief Network model is created using data from the training database; and, the fully unsupervised Bayesian Belief Network is validated. Clinical parameters are collected from an individual patient/donor; and, such clinical parameters are input into the fully unsupervised Bayesian Belief Network model via a graphical user interface. The patient-specific probability of disease is output from the fully unsupervised Bayesian Belief Network model and sent to the graphical user interface for use by a clinician in pre-operative organ matching. The fully unsupervised Bayesian Belief Network model is updated using the clinical parameters from the individual patient and the patient-specific probability of transplant survival. | 05-01-2014 |
20140122383 | METHOD AND SYSTEM FOR PSYCHOLOGICAL ANALYSIS BY FUSING MULTIPLE-VIEW PREDICTIONS - One embodiment of the present invention provides a system for predicting a personality trait. During operation, the system initially obtains personality data associated with users. The system collects sample data associated with the users. Next, the system trains a predictor with the collected sample data and the personality data. Then, the system collects data associated with a particular user, and generates a personality trait score for the particular user by using the predictor to analyze the particular user's collected data. | 05-01-2014 |
20140122384 | SYSTEM AND METHOD FOR VISUALLY TRACKING A LEARNED PROCESS - A system and method for visually tracking a learned process. The user input utilized to perform the learned process and a title for the learned processed is received. Nodes are created to represent steps of the learned process in response to the user input. Information for each of the steps is associated with each of the nodes. The nodes are connected in an order for performing the learned process. The connected nodes are visually displayed as a trail for one or more users to perform the learned process. | 05-01-2014 |
20140122385 | Statistical Data Learning Under Privacy Constraints - A computer-implemented method is provided for statistical data learning under privacy constraints. The method includes: receiving, by a processor, a plurality of pieces of statistical information relating to a statistical object and aggregating, by the processor, the plurality of pieces of statistical information so as to provide an estimation of the statistical object. Each piece of statistical information includes an uncertainty variable, the uncertainty variable being a value determined from a function having a predetermined mean. The number of pieces of statistical information aggregated is proportional to the reliability of the estimation of the statistical object. | 05-01-2014 |
20140122386 | DIFFERENTIAL DYNAMIC HOST CONFIGURATION PROTOCOL LEASE ALLOCATION - Disclosed is a novel passive fingerprinting technique based on DHCP messages to determine the device type and operating system. DHCP implementations are shown to vary among device types and have an effect on DHCP lease durations. To improve network address utilization, without introducing any protocol changes, the present invention provides a new leasing strategy which takes into account device types. This strategy, compared to current approaches, improves the address utilization sixfold without considerably increasing DHCP overhead. | 05-01-2014 |
20140122387 | PORTABLE WORKLOAD PERFORMANCE PREDICTION FOR THE CLOUD - A method is disclosed to perform performance prediction for cloud-based databases by building on a computer a cloud database performance model using one or more training workloads; and using the learned model on the computer to predict database performance in the cloud for a new workload. | 05-01-2014 |
20140122388 | QUERY GENERATION AND TIME DIFFERENCE FEATURES FOR SUPERVISED SEMANTIC INDEXING - Semantic indexing methods and systems are disclosed. One such method is directed to training a semantic indexing model by employing an expanded query. The query can be expanded by merging the query with documents that are relevant to the query for purposes of compensating for a lack of training data. In accordance with another exemplary aspect, time difference features can be incorporated into a semantic indexing model to account for changes in query distributions over time. | 05-01-2014 |
20140122389 | METHODS FOR PROCESSING CLINICAL INFORMATION - Described herein are methods for processing data in order to assess the likelihood that a patient belongs within a specified cohort. In general, the method may include the steps of receiving a plurality of data elements from multiple data sets, wherein at least a portion of the plurality of data elements are unstructured data elements; and assessing the likelihood that the patient belongs within the specified cohort using at least a portion of the plurality of data elements including at least one unstructured data element. In some embodiments, the method may further include the step of processing the unstructured data elements. In some embodiments, the method may further include the step of querying at least a portion of the plurality of data elements including at least one unstructured data element to assess the likelihood that the patient belongs within the specified cohort. | 05-01-2014 |
20140122390 | Systems and Methods for Conflict Resolution and Stabilizing Cut Generation in a Mixed Integer Program Solver - Systems and methods for conflict resolution and stabilizing cut generation in a mixed integer linear program (MILP) solver are disclosed. One disclosed method includes receiving a mixed integer linear problem (MILP), the MILP having a root node and one or more global bounds; pre-processing the MILP, the MILP being associated with nodes; establishing a first threshold for a learning phase branch-and-cut process; performing, by one or more processors, the learning phase branch-and-cut process for nodes associated with the MILP, wherein performing the learning phase branch-and-cut process includes: evaluating the nodes associated with the MILP, collecting conflict information about the MILP, and determining whether the first threshold has been reached; responsive to reaching the first threshold, removing all of the nodes and restoring a root node of the MILP; and solving, with the one or more processors, the MILP using the restored root node and the collected conflict information. | 05-01-2014 |
20140122391 | Top-Down Abstraction Learning Using Prediction as a Supervisory Signal - A method of machine learning for use with a learning machine which includes a first input sensor adapted to sense an environment, a first output controller adapted to act on the environment, and a computing system including a user input device, a memory, and a processor, includes the steps of providing an event set comprising one or more events, providing a model set adapted to comprise one or more models, and iteratively repeating a sequence of steps for augmenting the event set with the plurality of new events, and acting on the environment using the first output controller. | 05-01-2014 |
20140122392 | SENSOR DATA PROCESSING - A method and apparatus for processing data, the data including a set of one or more system inputs; and a set of one or more system outputs; wherein each system output corresponds to a respective system input. Each system input can include a plurality of data points, such that at least one of these data points is from a different data source to at least one other of those data points. The method includes performing a kernel function on a given system input from the data and a further system input to provide kernelised data; and inferring a value indicative of a significance of data from a particular data source; wherein the inferring includes applying a regression technique to the kernelised data. | 05-01-2014 |
20140122393 | INFORMATION PROCESSING SYSTEM, NETWORK STRUCTURE LEARNING DEVICE, LINK STRENGTH PREDICTION DEVICE, LINK STRENGTH PREDICTION METHOD AND PROGRAM - An information processing system separately generates sample sequences from a posterior distribution of each random variable in a probability model representing the structure of a template network that serves as a template for a plurality of networks whose network structures are to be learned, and from a posterior distribution of each random variable in a probability model representing the structures of the plurality of networks, using learning data and hyperparameters relating to the plurality of networks. Next, the information processing system derives a predictive value of the strength of a link specified by an external variable based on the external variable and on the sample sequences. | 05-01-2014 |
20140122394 | DIRECTED BEHAVIOR IN HIERARCHICAL TEMPORAL MEMORY BASED SYSTEM - A hierarchy of computing modules is configured to learn a cause of input data sensed over space and time, and is further configured to determine a cause of novel sensed input data dependent on the learned cause. At least one of the computing modules has a sequence learner module configured to associate sequences of input data received by the computing module to a set of causes previously learned in the hierarchy. | 05-01-2014 |
20140129490 | IMAGE URL-BASED JUNK DETECTION - Architecture that includes a junk (unwanted) image detection algorithm which performs junk image detection of unwanted images before the images are actually downloaded for indexing. Features are employed related to image location information and host websites, such as image path descriptor (e.g., URL-uniform resource locator) pattern features, webpage content features, click features, and image aggregated information in a machine learning based framework to predict the probability that an image is unwanted (or wanted) before the images are downloaded. The framework is then applied to build a statistical model and predict junk scores. By removing image URLs marked as “junk” from the work list of an automated indexer (e.g., crawler), the indexer bandwidth is significantly improved with a corresponding improvement in the publish rate. | 05-08-2014 |
20140129491 | EMPIRICAL MODELING WITH GLOBALLY ENFORCED GENERAL CONSTRAINTS - In certain embodiments, a method includes formulating an optimization problem to determine a plurality of model parameters of a system to be modeled. The method also includes solving the optimization problem to define an empirical model of the system. The method further includes training the empirical model using training data. The empirical model is constrained via general constraints relating to first-principles information and process knowledge of the system. | 05-08-2014 |
20140129492 | CONCEPT NOISE REDUCTION IN DEEP QUESTION ANSWERING SYSTEMS - Method, computer program product, and system to perform an operation for a deep question answering system. The operation begins by computing a concept score for a first concept in a first case received by the deep question answering system, the concept score being based on a machine learning concept model for the first concept. The operation then excludes the first concept from consideration when analyzing a candidate answer and an item of supporting evidence to generate a response to the first case upon determining that the concept score does not exceed a predefined concept minimum weight threshold. The operation then increases a weight applied to the first concept when analyzing the candidate answer and the item of supporting evidence to generate the response to the first case when the concept score exceeds a predefined maximum weight threshold. | 05-08-2014 |
20140129493 | Method and System for Visualizing Complex Data via a Multi-Agent Query Engine - An interactive and intelligent user interface for inputting a query, generating a query result including one or more matching concepts stored in a knowledgebase of one or more media types, and presenting the user with a rich personalized query result based on the user's preferences and personal information, and provides improved relevant search results. | 05-08-2014 |
20140129494 | SEARCHING TEXT VIA FUNCTION LEARNING - A method which does not rely on explicit inverted indices is provided to search for documents in a corpus of documents responsive to a textual search query. The method includes (a) selecting a program that is customized by setting values for a plurality of parameters, the program structured to receive the textual search query as input and to provide as output values indicating the relevance of the documents in the corpus to the search query; (b) training the program using a machine learning technique; and (c) applying the trained program to the textual search query. The program may be based on a structure that is developed based on a genetic programming technique. | 05-08-2014 |
20140136451 | Determining Preferential Device Behavior - Systems, methods and computer program products are disclosed for machine learning to determine preferential device behavior. In some implementations, a server receives inputs, including attributes from a client device, crowd-sourced data from a number of other devices and a priori knowledge. The server includes a concept engine that applies machine-learning process to the inputs. The output of the machine learning process is transported to the client device. At the client device, a client engine associates attributes observed at the device to the machine learning output to determine a user profile. Applications may access the user profile to determine preferential device behavior, such as provide targeted information to the user or take action on the device that is personalized to the user of the device. | 05-15-2014 |
20140136452 | PREDICTIVE ANALYTICS FACTORY - An apparatus, system, method, and computer program product are disclosed for a predictive analytics factory. A receiver module is configured to receive training data. A function generator module is configured to determine a plurality of learned functions from multiple classes based on the training data. A predictive compiler module is configured to form a predictive ensemble comprising a subset of learned functions from the plurality of learned functions. The subset of learned functions is from at least two of the multiple classes. | 05-15-2014 |
20140136453 | STATISTICAL ESTIMATION OF ORIGIN AND DESTINATION POINTS OF TRIP USING PLURALITY OF TYPES OF DATA SOURCES - A method of predicting the origin and destination points of an unknown trip using a computer includes receiving an input of second marker information including the type and position of a known marker included in a second region; generating a second feature vector at each spot included in the second region on the basis of the second marker information; and predicting the probability that the respective spots included in the second region are the origin and destination points on the basis of a prediction model, which is acquired based on first marker information including the type and position of a known marker included in a first region and information on the known origin and destination points included in the first region, and the second feature vector. | 05-15-2014 |
20140136454 | PREDICTING CHANGE-POINTS IN CONTINUOUS TIME-SERIES DATA - A method for predicting a change-point in continuous time-series data by computer includes accepting an input of continuous time-series data in past w periods; and determining whether or not a change-point in time-series appears in future k periods; wherein determining further includes determining, by a plurality of preliminary classifiers on the basis of the continuous time-series data in the past w periods and their respective preliminary classification rules, whether or not the change-point appears in future (k+i) periods, where i=0, 1, . . . , m; and determining, by a secondary classifier on the basis of determinations made by the plurality of preliminary classifiers and a secondary classification rule, whether or not the change-point appears in future c periods. | 05-15-2014 |
20140136455 | Self-Organizing Sensing and Actuation for Automatic Control - A Self-Organizing Process Control Architecture is introduced with a Sensing Layer, Control Layer, Actuation Layer, Process Layer, as well as Self-Organizing Sensors (SOS) and Self-Organizing Actuators (SOA). A Self-Organizing Sensor for a process variable with one or multiple input variables is disclosed. An artificial neural network (ANN) based dynamic modeling mechanism as part of the Self-Organizing Sensor is described. As a case example, a Self-Organizing Soft-Sensor for CFB Boiler Bed Height is presented. Also provided is a method to develop a Self-Organizing Sensor. | 05-15-2014 |
20140136456 | MODELER FOR PREDICTING STORAGE METRICS - Described herein is a system and method for dynamically managing service-level objectives (SLOs) for workloads of a cluster storage system. Proposed states/solutions of the cluster may be produced and evaluated to select one that achieves the SLOs for each workload. A planner engine may produce a state tree comprising nodes, each node representing a proposed state/solution. New nodes may be added to the state tree based on new solution types that are permitted, or nodes may be removed based on a received time constraint for executing a proposed solution or a client certification of a solution. The planner engine may call an evaluation engine to evaluate proposed states, the evaluation engine using an evaluation function that considers SLO, cost, and optimization goal characteristics to produce a single evaluation value for each proposed state. The planner engine may call a modeler engine that is trained using machine learning techniques. | 05-15-2014 |
20140136457 | SYSTEM AND METHOD FOR USING PATTERN RECOGNITION TO MONITOR AND MAINTAIN STATUS QUO - The present invention relates to a method of checking data gathered from a content source comprising: receiving initial data from the content source; training a data profiler to generate a set of trusted constraint modules, said training comprising (1) selecting constraint modules having parameters that are applicable to the initial data, (2) adjusting the parameters of the applicable constraint modules to conform with new data from the content source, (3) identifying non-stable constraint modules, and (4) generating a set of trusted constraint modules by removing the non-stable constraint modules; applying the set of trusted constraint modules to subsequently received data from the content source to determine whether the subsequently received data meets the parameters of the set of trusted constraint modules; and signaling a failure upon the subsequently received data failing to meet the parameters of the set of trusted constraint modules. | 05-15-2014 |
20140143181 | SYSTEMS AND METHODS FOR PROVIDING NETWORK OBJECTS THAT MOVE FREELY, EXPAND KNOWLEDGE AUTONOMOUSLY, AND COMMUNICATE WITH USERS OF A NETWORK - A method for providing information to a user of a computer in a network of computers via an object program which moves freely about the network to access and store data relating to knowledge attributes of property fields available in the network, expanding autonomously the knowledge attributes over time as the object moves about the network, and communicating the information of the knowledge attributes to and from the computers of requestors as requested. | 05-22-2014 |
20140143182 | SIMILARITY ANALYSIS WITH TRI-POINT DATA ARBITRATION - Systems, methods, and other embodiments associated with similarity analysis using tri-point arbitration are described. In one embodiment, a method includes selecting a data point pair and an arbiter point from a data set. A tri-point arbitration coefficient (ρTAC) is calculated for data point pairs based, at least in part, on a distance between the first and second data points and the arbiter point. A similarity metric is determined for the data set based, at least in part, on an aggregation of tri-point arbitration coefficients for data point pairs in the set of data points using the selected arbiter point. | 05-22-2014 |
20140143183 | HIERARCHICAL MODEL FOR HUMAN ACTIVITY RECOGNITION - The disclosure provides an approach for recognizing and analyzing activities. In one embodiment, a learning application trains parameters of a hierarchical model which represents human (or object) activity at multiple levels of detail. Higher levels of detail may consider more context, and vice versa. Further, learning may be optimized for a user-preferred type of inference by adjusting a learning criterion. An inference application may use the trained model to answer queries about variable(s) at any level of detail. In one embodiment, the inference application may determine scores for each possible value of the query variable by finding the best hierarchical event representation that maximizes a scoring function while fixing the value of the query variable to its possible values. Here, the inference application may approximately determine the best hierarchical event representation by iteratively optimizing one level-of-detail variable at a time while fixing other level-of-detail variables, until convergence. | 05-22-2014 |
20140143184 | TURN RESTRICTION INFERENCING - Architecture that extracts turn restrictions from geolocation traces both offline and online (in realtime). By identifying from the location traces which specific turns a driver takes and at which points in time, turn restrictions and associated time-dependence can be mined (inferred). Turn restrictions can be inferred based on the nature of drivers who tend to take the shortest route. The architecture can infer allowed turns and turn restrictions by mining user location traces, infer turn restrictions and associated confidence scores by comparing the routes followed by users with the routes that are shortest when applying the set of known turn restrictions, and infer turn restrictions based on the accessibility criterion such as each road section (between two adjacent intersections) is accessible in at least one way. A scoring method is provided for calculating the probability for a turn restriction to exist by fusing the scores described above with statistical information. | 05-22-2014 |
20140143185 | METHOD AND APPARATUS FOR INFERRING LOGICAL DEPENDENCIES BETWEEN RANDOM PROCESSES - Certain aspects of the present disclosure relate to methods and apparatus for inferring causal relationship between random processes using a temporal learning algorithm. The temporal learning algorithm determines structure of a causal graph with a set of nodes. Input to the nodes may be binary time series (e.g., random processes). The output of the temporal learning algorithm may be a labeled directed graph in which the direction of the connection between each two node indicates causal direction and the strength of connectivity between the nodes indicates intensity of the causal influence. The temporal learning algorithm may iteratively update strength of connections between nodes to track variations in real time. | 05-22-2014 |
20140143186 | HYBRID CLUSTERING FOR DATA ANALYTICS - The invention includes methods and systems for analyzing data to determine trends in the data and to identify outliers. The methods and systems include a learning algorithm whereby a data space is co-populated with artificial, evenly distributed data, and then the data space is carved into smaller portions whereupon the number of real and artificial data points are compared. Through an iterative process, clusters having less than evenly distributed real data are discarded. Additionally, a final quality control measurement is used to merge clusters that are too similar to be meaningful. The invention is widely applicable to data analytics, generally, including financial transactions, retail sales, elections, and sports. | 05-22-2014 |
20140143187 | METHOD FOR PROBABILISTICALLY PREDICTING LOCATION OF OBJECT - Provided is a method for predicting location of an object by using a learning model which includes an input layer, a hidden layer, and an output layer each including one or more nodes, that are associated with each other by a weight. When a definitive time value is input to the input layer, the output layer is configured to output a probability value that the object is located at a specific place. | 05-22-2014 |
20140143188 | METHOD OF MACHINE LEARNING, EMPLOYING BAYESIAN LATENT CLASS INFERENCE: COMBINING MULTIPLE GENOMIC FEATURE DETECTION ALGORITHMS TO PRODUCE AN INTEGRATED GENOMIC FEATURE SET WITH SPECIFICITY, SENSITIVITY AND ACCURACY - BAYSIC (BAYesian System for Integrated Combination) combines sets of genomic and other biological data features to optimize selected data feature attributes, for example, detecting genome variants including single nucleotide variants (SNVs) and small insertion/deletions in genomes. The present disclosure presents one possible embodiment employing BAYSIC to combine single nucleotide variants detected by several distinct variant calling methods into an integrated SNV call set that is more accurate than any single SNV calling method or any ad hoc method of combining call sets. BAYSIC is a, tested and validated method using unsupervised machine learning, employing Bayesian latent class inference to combine variant sets produced by different packages. | 05-22-2014 |
20140143189 | ON-DEMAND POWER CONTROL SYSTEM, ON-DEMAND POWER CONTROL SYSTEM PROGRAM, AND COMPUTER-READABLE RECORDING MEDIUM RECORDING THE SAME PROGRAM - In order to estimate behavior of a person at home from a feature and a power consumption pattern of an electrical device, an on-demand power control system of the present invention includes initial human-induced probability value estimation means that estimates a state of the electrical device, and estimates an initial value of a human-induced probability of the electrical device based on the estimated state of the electrical device, human position estimation means for calling up the initial value of the human-induced probability of the electrical device and a likelihood map of this device from a memory, performing, for all samples, a process of referring to a sample human position selected from the likelihood map and calculating a weight of the device by multiplying a human position and the human-induced probability of the device, and estimating a probability of a human position at each time point until a final time; and human-induced probability re-estimation means for performing recalculation of the human-induced probability based on the human-induced probability and a human position probability, performing the recalculation of the human-induced probability until a value of the recalculation converges, and outputting the human-induced probability and the human position probability when the value converges. | 05-22-2014 |
20140149322 | Protecting Contents in a Content Management System by Automatically Determining the Content Security Level - An approach is provided to automatically classify and handle data. The approach is implemented by an information handling system. In the approach, data is received, from a sender, at a content management system. When the data is received, the system automatically utilizes an artificial intelligence (AI) engine (e.g., IBM Watson, etc.) to perform an unstructured information analysis using a pre-existing knowledge base. The result of using the AI engine is an identification of a confidentiality level of the data. The approach further performs an action based on the identified confidentiality level of the data. | 05-29-2014 |
20140149323 | SYSTEM, METHOD AND COMPUTER PROGRAM PRODUCT FOR INCREMENTAL LEARNING OF SYSTEM LOG FORMATS - A method for learning a data format is disclosed including but not limited to inputting an initial description of a data format and a batch of data comprising data in a new data format not covered by the initial description, instructions to use the first description to parse the records in the data source; discarding records in the input data that parse successfully, instructions to collect records that fail to parse, instructions to accumulate a quantity, M of records that fail to parse, returning a modified description that extends the initial description to cover the new data, transforming the first description, D into a second description D′ to accommodate differences between the input data format and the first description D by introducing options where a piece of data was missing in the input data and introducing unions where a new type of data was found in the input data. | 05-29-2014 |
20140149324 | Method and System to Manage Complex Systems Knowledge - Described herein are a method and system for managing complex systems knowledge. Information generated during operation of a complex system is monitored. This information is normalized to a complex system base element that is expressed according to a standardized element taxonomy. During normalization, the information inherits characteristics of the base element. Following normalization, the information is stored in an information database. This information can be used to do any one or more of design, construct, operate, automate and otherwise configure another complex system. | 05-29-2014 |
20140149325 | SYSTEM MONITOR AND METHOD OF SYSTEM MONITORING - A method of system monitoring or, more particularly, novelty detection, based on extreme value theory in particular a points-over-threshold POT method which is applicable to multimodal multivariate data. Multimodal multivariate data points collected by continuously monitoring a system are transformed into probability space by obtaining their probability density function (pdf) values from a statistical model of normality, such as a pdf fitted to a training data set of normal data. Extremal data is defined as that whose pdf value is below a predetermined threshold and a new analytic function, in particular the Generalised Pareto Distribution (GPD) is fitted to that extremal data only. The fitted GPD can be compared to a GPD fitted to the extremal datapoints of the training data set of normal data to determine if the monitored system is in a normal state. Alternatively a threshold can be set by calculating an extreme value distribution of the GPD fitted to the extremal data of the training data set and setting as the threshold the pdf value which separates a desired proportion, e.g., 0.99 of the probability mass from the remainder. If the minimum pdf value of a set of data points collected from the system is below the threshold, the system may be abnormal. | 05-29-2014 |
20140156565 | METHODS AND SYSTEMS FOR PREDICTING LEARNING CURVE FOR STATISTICAL MACHINE TRANSLATION SYSTEM - The disclosed embodiments relate to a system and method for predicting the learning curve of an SMT system. A set of anchor points are selected. The set of anchor points correspond to a size of a corpus. Thereafter, a gold curve or a benchmark curve is fitted based on the set of anchor points to determine the BLEU score. Based on the BLEU score and a set of parameters associated with the first set of anchor points, a confidence score is computed. | 06-05-2014 |
20140156566 | CUSTOMIZED PREDICTORS FOR USER ACTIONS IN AN ONLINE SYSTEM - Online systems generate predictors for predicting actions of users of the online system. The online system receives requests to generate predictor models for predicting whether a user is likely to take an action of a particular action type. The request specifies the type of action and criteria for identifying a successful instance of the action type and a failure instance of the action type. The online system collects data including successful and failure instances of the action type. The online system generates one or more predictors of different types using the generated data. The online system evaluates and compares the performance of the different predictors generated and selects a predictor based on the performance. The online system returns a handle to access the generated predictor to the requester of the predictor. | 06-05-2014 |
20140156567 | SYSTEM AND METHOD FOR AUTOMATIC DOCUMENT CLASSIFICATION IN EDISCOVERY, COMPLIANCE AND LEGACY INFORMATION CLEAN-UP - A system, method and computer program product for automatic document classification, including an extraction module configured to extract structural, syntactical and/or semantic information from a document and normalize the extracted information; a machine learning module configured to generate a model representation for automatic document classification based on feature vectors built from the normalized and extracted semantic information for supervised and/or unsupervised clustering or machine learning; and a classification module configured to select a non-classified document from a document collection, and via the extraction module extract normalized structural, syntactical and/or semantic information from the selected document, and generate via the machine learning module a model representation of the selected document based on feature vectors, and match the model representation of the selected document against the machine learning model representation to generate a document category, and/or classification for display to a user. | 06-05-2014 |
20140156568 | SELF LEARNING ADAPTIVE MODELING SYSTEM - Self-learning and adaptive modeling is employed with respect to predictive analytics. A hierarchical model structure can be employed comprising a set of predictive models automatically built from accumulated data and distributed across multiple levels. For a given input type, a set of candidate models can be identified across varying levels of granularity, and a best model selected based on a comparison of performance metrics of the models. The best model can then be activated for use in making predictions. Of course, the best model can change based on most recent training performance results, since as more data becomes available more specific models can be developed. | 06-05-2014 |
20140156569 | METHOD AND APPARATUS FOR IMPROVING RESILIENCE IN CUSTOMIZED PROGRAM LEARNING NETWORK COMPUTATIONAL ENVIRONMENTS - An apparatus and a method are provided for learning a program with a large number of parameters. In one embodiment, a method not only distorts the input values, but also distorts some of the parameters in the program model. Such an approach not only forces the learned program to acquire parameter values to predict missing or desired data, but also to correct errors in the input data and the program parameters themselves, thereby rendering the learned program more resilient to overfitting and falling into local optima. | 06-05-2014 |
20140156570 | METHOD AND DEVICE FOR PREDICTING THE CONDITION OF A COMPONENT OR SYSTEM, COMPUTER PROGRAM PRODUCT - A device method and computer program product are disclosed for trend prediction of the course of a time-dependent series of data points of a component or system, particularly for an aircraft or spacecraft, including: providing an optimised decision tree, the input node of which is provided for inputting an input vector, the nodes of which contain the data points of a respective input vector and the leaves of which each contain an extrapolation function; iteratively calculating future data points by a respective time-dependent series of data points being inputted into the decision tree as an input vector and the decision tree calculating therefrom a data point subsequent to the last data point of the input vector in an automated manner, the calculated subsequent data point being added to the time-dependent series of data points in order to be used as a new input vector for the next iteration step. | 06-05-2014 |
20140156571 | TOPIC MODELS - Machine learning techniques may be used to train computing devices to understand a variety of documents (e.g., text files, web pages, articles, spreadsheets, etc.). Machine learning techniques may be used to address the issue that computing devices may lack the human intellect used to understand such documents, such as their semantic meaning. Accordingly, a topic model may be trained by sequentially processing documents and/or their features (e.g., document author, geographical location of author, creation date, social network information of author, and/or document metadata). Additionally, as provided herein, the topic model may be used to predict probabilities that words, features, documents, and/or document corpora, for example, are indicative of particular topics. | 06-05-2014 |
20140156572 | AUTOMATIC IDENTIFICATION OF INFORMATION USEFUL FOR GENERATION-BASED FUNCTIONAL VERIFICATION - A computer-implemented method, an apparatus and a computer program for automatically extracting useful information for functional verification. The method comprising performing repeatedly both operating an instruction generator associated with a Design Under Test (DUT), whereby a generated instruction is determined, the generated instruction having one or more instruction attributes; and collecting information relating to the generated instruction. Based on the generated instruction and the collected information, a classification technique is utilized to classify the information based on the instruction attributes. | 06-05-2014 |
20140156573 | METHODS FOR GENERATING PREDICTIVE MODELS FOR EPITHELIAL OVARIAN CANCER AND METHODS FOR IDENTIFYING EOC - A method for generating a model for epithelial ovarian cancer is presented, comprising the steps of obtaining a mass spectrum for each of a plurality of samples, segmenting each of the mass spectra into “bins,” and determining a plurality of relationships between two or more bins. One are more statistically significant factors are identified according to the determined plurality of relationships, and a predictive model is generated as a function of the one or more identified factors. A method of the present invention may further comprise the step of obtaining one or more nuclear magnetic resonance spectra of each of the samples, which are segmented into a plurality of bins. Combinations of mass spectra and NMR spectra may be used to determine the plurality of relationships. In other embodiments, methods for identifying the presence of EOC indicated by a biological sample of an individual are presented. | 06-05-2014 |
20140164297 | GENERATING TRAINING DOCUMENTS - A method of generating training documents for training a classifying device comprises, with a processor, sampling from a distribution of words in a number of original documents, and creating a number of pseudo-documents from the distribution of words, the pseudo-documents comprising a similar distribution of words as the original documents. A device for classifying textual documents comprises a processor; and a memory communicatively coupled to the processor, the memory comprising a sampling module to, when executed by the processor, determine the distribution of words in a number of original documents, a pseudo-document creation module to, when executed by the processor, create a number of pseudo-documents from the distribution of words, the pseudo-documents comprising a similar distribution of words as the original documents, and a training module to, when executed by the processor, train the device to classify textual documents based on the pseudo-documents. | 06-12-2014 |
20140164298 | SYSTEM AND METHOD FOR ONTOLOGY DERIVATION - A system for deriving ontologies to support inferencing with changing context, including changes of time. Embodiment of the invention use a unique system for modeling context and the interactions among multiple contexts in order to compute functions that can modify ontologies for presentation to a reasoning system. A parallel unique system allows previous inferences to be retrospectively modified based on newly derived ontological semantics. The system allows for the creation of new ontological elements and auditable models of agency and cause. It can be implemented using methods that delay evaluation until semantic interpretation is required, either at the ontological or inferential level. | 06-12-2014 |
20140172753 | RESOURCE ALLOCATION FOR MACHINE LEARNING - Resource allocation for machine learning is described such as for selecting between many possible options, for example, as part of an efficient training process for random decision tree training, for selecting which of many families of models best describes data, for selecting which of many features best classifies items. In various examples samples of information about uncertain options are used to score the options. In various examples, confidence intervals are calculated for the scores and used to select one or more of the options. In examples, the scores of the options may be bounded difference statistics which change little as any sample is omitted from the calculation of the score. In an example, random decision tree training is made more efficient whilst retaining accuracy for applications not limited to human body pose detection from depth images. | 06-19-2014 |
20140172754 | SEMI-SUPERVISED DATA INTEGRATION MODEL FOR NAMED ENTITY CLASSIFICATION - According to one embodiment, a semi-supervised data integration model for named entity classification from a first repository of entity information in view of an auxiliary repository of classification assistance data is provided. Training data are compared to named entity candidates taken from the first repository to form a positive training seed set. A decision tree is populated and classification rules are created for classifying the named entity candidates. A number of entities are sampled from the named entity candidates. The sampled entities are labeled as positive examples and/or negative examples. The positive training seed set is updated to include identified commonality between the positive examples and the auxiliary repository. A negative training seed set is updated to include negative examples which lack commonality with the auxiliary repository. In view of both the updated positive and negative training seed sets, the decision tree and the classification rules are updated. | 06-19-2014 |
20140172755 | MULTI-DIMENSIONAL FEATURE MERGING FOR SUPPORTING EVIDENCE IN A QUESTION AND ANSWERING SYSTEM - Method, system, and computer program product to analyze a plurality of candidate answers identified as responsive to a question presented to a deep question answering system, by computing a first feature score for a first feature of an item of evidence, of a plurality of items of evidence, the first feature score being based on at least one attribute of the first feature, the item of evidence relating to a first candidate answer, of the plurality of candidate answers, and computing a merged feature score for the first candidate answer by applying the first feature score to a second feature score for a second feature of the item of evidence. | 06-19-2014 |
20140172756 | QUESTION CLASSIFICATION AND FEATURE MAPPING IN A DEEP QUESTION ANSWERING SYSTEM - System, method, and computer program product to identify relevant features in a deep question answering system, by classifying a first case received by the deep question answering system, and, while training the deep question answering system to answer the first case, identifying a first feature in the first case, computing a first feature score for the first feature, the first feature score indicating a relevance of the first feature in generating a correct response to the first case, and, identifying the first feature as relevant in answering the classified first case upon determining that the first feature score exceeds a relevance threshold. | 06-19-2014 |
20140172757 | System and Method For Learning Answers To Frequently Asked Questions From a Semi-Structured Data Source - A frequently-asked-question (FAQ)-based system receives question(s) from a user and generates answer(s) based on data about the question(s). In one embodiment, a method includes retrieving, from a memory, a global structure and candidate answers therein. The method can include computing a first, second, and third probability of a candidate answer based on a local structure of the candidate answer within the global structure, content of the candidate answer given content of a query and context of the candidate answer given the content of the query, respectively. The method can include providing a combined probability of the candidate answer based on the first probability, second probability, and third probability. The method can improve efficiency of a FAQ-based system by automating organization of semi-structured data in a database. Therefore, a human user does not need to manually generate the database when it is already generated in semi-structured form, a semi-structured HTML document. | 06-19-2014 |
20140172758 | PERSONAL EMERGENCY RESPONSE SYSTEM BY NONINTRUSIVE LOAD MONITORING - A method for a personal emergency response system includes receiving output signals of a nonintrusive load monitoring (NILM)system coupled to an electrical supply of an person's residence, the output signals indicating switching events of appliances connected to the electrical supply. A computer processor is then used to process the output signals in accordance with a machine learning algorithm to identify appliance activation routines. Rules are defined based on the identified appliance activation routines, and the computer processor is used to monitor the output signals and apply the rules to the output signals to identify appliance switching conditions that violate the rules. | 06-19-2014 |
20140172759 | INTELLIGENT ELECTRONIC MONITORING SYSTEM - A method of monitoring a position of a moveable entity includes equipping a moveable entity with a position sensor that outputs position signals indicating current geographical positions of the sensor, and using a machine learning system to process the position signals in accordance with a machine learning algorithm to identify reference positions indicated by the position signals corresponding to a first type of activity performed by the entity. Rules are defined based on the identified reference positions. The computer processor then monitors the position signals and apply the rules to the position signals to identify positions that violate the rules. | 06-19-2014 |
20140172760 | USER INTERFACE AND WORKFLOW FOR PERFORMING MACHINE LEARNING - A computing device receives a training data set that includes a plurality of positive examples of sensitive data and a plurality of negative examples of sensitive data. The computing device analyzes the training data set using machine learning to generate a machine learning-based detection (MLD) profile that can be used to classify new data as sensitive data or as non-sensitive data. The computing device computes a quality metric for the MLD profile. | 06-19-2014 |
20140180972 | METHOD AND APPARATUS FOR PROVIDING BEHAVIORAL PATTERN GENERATION FOR MIXED REALITY OBJECTS - An approach is provided for behavioral pattern generation for mixed reality objects. A mixed reality platform determines one or more computation closures for describing one or more user behavioral patterns associated with one or more digital objects of at least one augmented reality information space. The mixed reality platform then processes and/or facilitates a processing of one or more interactions with the one or more digital objects, one or more augmented reality applications associated with the at least one augmented reality information space, or a combination thereof to cause, at least in part, a determination of (a) the one or more user behavioral patterns from the one or more interactions, (b) the data acted on by the one or more computation closures, or (c) a combination thereof. | 06-26-2014 |
20140180973 | Iterative Active Feature Extraction - Techniques for iterative feature extraction using domain knowledge are provided. In one aspect, a method for feature extraction is provided. The method includes the following steps. At least one query to predict at least one future value of a given value series based on a statistical model is received. At least two predictions of the future value are produced fulfilling at least the properties of 1) each being as probable as possible given the statistical model and 2) being mutually divert (in terms of numerical distance measure). A user is queried to select one of the predictions. The user may be queried for textual annotations for the predictions. The annotations may be used to identify additional covariates to create an extended set of covariates. The extended set of covariates may be used to improve the accuracy of the statistical model. | 06-26-2014 |
20140180974 | Transaction Risk Detection - The current subject matter describes scoring of transactions associated with a profiling entity so as to determine risk associated with the transactions. Data characterizing at least one new transaction can be received. A latent dirichlet allocation (LDA) model trained on historical data can be obtained. Based on new words in the received data, the LDA model can update a topic probability mixture vector. Based on the updated topic probability mixture vector, numerical values of one or more predictive features can be calculated. Based on the numerical values of the one or more predicted features, the at least one transaction in the received data can be scored. Related apparatus, systems, techniques and articles are also described. | 06-26-2014 |
20140180975 | INSTANCE WEIGHTED LEARNING MACHINE LEARNING MODEL - An instance weighted learning (IWL) machine learning model. In one example embodiment, a method of employing an IWL machine learning model to train a classifier may include determining a quality value that should be associated with each machine learning training instance in a temporal sequence of reinforcement learning machine learning training instances, associating the corresponding determined quality value with each of the machine learning training instances, and training a classifier using each of the machine learning training instances. Each of the machine learning training instances includes a state-action pair and is weighted during the training based on its associated quality value using a weighting factor that weights different quality values differently such that the classifier learns more from a machine learning training instance with a higher quality value than from a machine learning training instance with a lower quality value. | 06-26-2014 |
20140180976 | SYSTEMS AND METHODS FOR GENERATING LEADS IN A NETWORK BY PREDICTING PROPERTIES OF EXTERNAL NODES - The present invention is directed towards systems and methods for predicting one or more desired properties of external nodes or properties of their relations with internal nodes, based on a selected group of nodes about which it is known whether the nodes have the desired properties, or it is known whether they have a desired relation property with an internal node. The method comprises storing in one or more data structures a first data set regarding external nodes and a second data set regarding nodes with known properties in a selected group, each data set having one or more data items representing one or more events relating to or attributes of each node in the data set, the second data set including one or more types of data items not included in the first data set. The method then models the second data set to identify from the second data one or more modeled events or attributes of internal nodes in the selected group that are statistically likely to identify the nodes or their relations, that have the desired properties and predicts which of the external nodes are statistically likely to have the one or more desired properties, or desired relation property with internal node, based on the identified plurality of modeled events or attributes and the events or attributes in the first data set. | 06-26-2014 |
20140180977 | Computationally Efficient Whole Tissue Classifier for Histology Slides - Systems and methods are disclosed for classifying histological tissues or specimens with two phases. In a first phase, the method includes providing off-line training using a processor during which one or more classifiers are trained based on examples, including: finding a split of features into sets of increasing computational cost, assigning a computational cost to each set; training for each set of features a classifier using training examples; training for each classifier, a utility function that scores a usefulness of extracting the next feature set for a given tissue unit using the training examples. In a second phase, the method includes applying the classifiers to an unknown tissue sample with extracting the first set of features for all tissue units; deciding for which tissue unit to extract the next set of features by finding the tissue unit for which a score: S=U−h*C is maximized, where U is a utility function, C is a cost of acquiring the feature and h is a weighting parameter; iterating until a stopping criterion is met or no more feature can be computed; and issuing a tissue-level decision based on a current state. | 06-26-2014 |
20140180978 | INSTANCE WEIGHTED LEARNING MACHINE LEARNING MODEL - An instance weighted learning (IWL) machine learning model. In one example embodiment, a method of employing an IWL machine learning model may include identifying a temporal sequence of reinforcement learning machine learning training instances with each of the training instances including a state-action pair, determining a first quality value for a first training instance in the temporal sequence of reinforcement learning machine learning training instances determining a second quality value for a second training instance in the temporal sequence of reinforcement learning machine learning training instances, associating the first quality value with the first training instance, and associating the second quality value with the second training instance. In this example embodiment, the first quality value is higher than the second quality value. | 06-26-2014 |
20140180979 | INTERESTINGNESS RECOMMENDATIONS IN A COMPUTING ADVICE FACILITY - The present disclosure provides a recommendation to a user through a computer-based advice facility, comprising collecting topical information, wherein the collected topical information includes an interestingness aspect; filtering the collected topical information based on the interestingness aspect; determining an interestingness rating from the collected topical information, wherein the determining is through the computer-based advice facility; and providing a user with the recommendation related to the topical information based on the interestingness rating. | 06-26-2014 |
20140180980 | INFORMATION IDENTIFICATION METHOD, PROGRAM PRODUCT, AND SYSTEM - In a case where supervised (learning) data is prepared and the case where test data is prepared, the data is recorded with time information attached to the data. The method includes clustering the learning data in a target class and clustering the test data in the target class. Then, the probability density for each of identified subclasses is calculated for each of time intervals having various time points and widths for the learning data, and is calculated for each of time intervals in the latest time period which have various widths, for the test data. Then, a ratio between a probability density obtained when learning is performed and a probability density obtained when testing is performed is obtained as a relative frequency in each of the time intervals for each of the subclasses. Input having a relative frequency that statistically and markedly increases is detected as an anomaly. | 06-26-2014 |
20140180981 | SYSTEM AND METHODS FOR COMPUTERIZED MACHINE-LEARNING BASED AUTHENTICATION OF ELECTRONIC DOCUMENTS INCLUDING USE OF LINEAR PROGRAMMING FOR CLASSIFICATION - Electronic document classification comprising providing training documents sorted into classes; linear programming including selecting inputs which maximize an output, given constraints on inputs, the output maximized being a difference between: a. first estimated probability that a document instance will be correctly classified, by a classifier corresponding to given inputs, as belonging to its own class, and b. second estimated probability that document instance will be classified, by the classifier, as not belonging to its own class; and classifying electronic document instances into classes, using a preferred classifier corresponding, to the inputs selected by the linear programming. A computerized electronic document forgery detection method provides training documents and uses a processor to select value-ranges of non-trivial parameters, such that selected values-range(s) of parameters are typical to an authentic document of given class, and atypical to a forged document of same class. | 06-26-2014 |
20140188767 | Computer-Implemented Methods and Systems for Determining Fleet Conditions and Operational Management Thereof - A method for determining fleet conditions and operational management thereof, performed by a central system includes receiving fleet data from at least one distributed data repository. The fleet data is substantially representative of information associated with a fleet of physical assets. The method also includes processing the received fleet data for the fleet using at least one process of a plurality of processes. The plurality of processes assess the received fleet data into processed fleet data. The method additionally includes determining a fleet condition status using the processed fleet data and the at least one process of the plurality of processes. The method further includes generating a fleet response. The fleet response is substantially representative of a next operational step for the fleet of physical assets. The method also includes transmitting the fleet response to at least one of a plurality of fleet response recipients. | 07-03-2014 |
20140188768 | System and Method For Creating Customized Model Ensembles On Demand - A computer-implemented system for creating customized model ensembles on demand is provided. An input module is configured to receive a query. A selection module is configured to create a model ensemble by selecting a subset of models from a plurality of models, wherein selecting includes evaluating an aspect of applicability of the models with respect to answering the query. An application module is configured to apply the model ensemble to the query, thereby generating a set of individual results. A combination module is configured to combine the set of individual results into a combined result and output the combined result, wherein combining the set of individual results includes evaluating performance characteristics of the model ensemble relative to the query. | 07-03-2014 |
20140188769 | SEISMIC DATA ANALYSIS - An approach for seismic data analysis is provided. In accordance with various embodiments, the active learning approaches are employed in conjunction with an analysis algorithm that is used to process the seismic data. Algorithms that may employ such active learning include, but are not limited to, ranking algorithms and classification algorithms. | 07-03-2014 |
20140195465 | MONITOR-MINE-MANAGE CYCLE - A monitor-mine-manage cycle is described, for example, for managing a data center, a manufacturing process, an engineering process or other processes. In various example, the following steps are performed as a continuous automated loop: receiving raw events from an observed system; monitoring the raw events and transforming them into complex events; mining the complex events and reasoning on results; making a set of proposed actions based on the mining; and managing the observed system by applying one or more of the proposed actions to the system. In various examples, the continuous automated loop proceeds while raw events are continuously received from the observed system and monitored. In some examples an application programming interface is described comprising programming statements which allow a user to implement a monitor-mine-manage loop. | 07-10-2014 |
20140195466 | INTEGRATED MACHINE LEARNING FOR A DATA MANAGEMENT PRODUCT - Apparatuses, systems, methods, and computer program products are disclosed for machine learning in a data management product. The apparatus includes an input module, a learned function module, and a results module. The input module is configured to receive an analysis request for the data management product. The learned function module is configured to execute one or more machine learning ensembles to predict one or more unknown values for the data management product. The result module is configured to provide native access, within the data management product, to the one or more unknown values. | 07-10-2014 |
20140195467 | BEHAVIOR HISTORY MANAGEMENT SYSTEM, AND BEHAVIOR HISTORY MANAGEMENT METHOD - Movement histories of a vehicle are registered appropriately in a database. A point specification unit specifies a geographical base of a user as a point serving as a nucleus of behavior of the user. When the need arises to delete a movement history stored in the database, a deletion subject data determination unit specifies a movement history having a low degree of relatedness to the geographical base of the user. A data deletion unit then deletes the movement history specified by the deletion subject data determination unit from the database. As a result, behavior histories having a high utility value to the user are held preferentially in the database. | 07-10-2014 |
20140201111 | CONFIDENTIALITY CLASSIFICATION OF FILES - A method for confidentiality classification of files includes vectorizing a file to reduce the file to a single structured representation; and analyzing the single structured representation with a machine learning engine that generates a confidentiality classification for the file based on previous training. A system for confidentiality classification of files includes a file vectorization engine to vectorize a file to reduce the file to a single structured representation; and a machine learning engine to receive the single structured representation of the file and generate a confidentiality classification for the file based on previous training. | 07-17-2014 |
20140201112 | ROBOT TEACHING SYSTEM AND ROBOT TEACHING METHOD - This robot teaching system includes a teaching tool including an operation portion operated by a user to specify teaching positions and specifying the teaching positions, a measuring portion measuring positions and postures of the teaching tool, and a control portion determining the teaching positions for a robot. The robot teaching system is configured to specify the teaching positions continuously while the user operates the operation portion of the teaching tool. | 07-17-2014 |
20140201113 | Automatic Genre Determination of Web Content - A mechanism is provided for automatic genre determination of web content. For each type of web content genre, a set of relevant feature types are extracted from collected training material, where genre features and non-genre features are represented by tokens and an integer counts represents a frequency of appearance of the token in both a first type of training material and a second type of training material. In a classification process, fixed length tokens are extracted for relevant features types from different text and structural elements of web content. For each relevant feature type, a corresponding feature probability is calculated. The feature probabilities are combined to an overall genre probability that the web content belongs to a specific trained web content genre. A genre classification result is then output comprising at least one specific trained web content genre to which the web content belongs together with a corresponding genre probability. | 07-17-2014 |
20140201114 | DEVICE OF MANAGING DISTRIBUTED PROCESSING AND METHOD OF MANAGING DISTRIBUTED PROCESSING - Provided is a device of managing distributed processing, including: a selecting unit that estimates a total execution time on the basis of each of distributed-execution patterns indicating a grouping mode for plural computers and corresponding to the number of computers that are in charge of each of processes having different parameters in plural phases, this total execution time being necessary for the plural computers to execute the plural processes in a distributed manner, thereby selecting a distributed-execution pattern that makes the total execution time minimal, from among the distributed-execution patterns. | 07-17-2014 |
20140207710 | TRANSDUCTIVE LASSO FOR HIGH-DIMENSIONAL DATA REGRESSION PROBLEMS - Various embodiments select features from a feature space. In one embodiment, a set of training samples and a set of test samples are received. A first centered Gram matrix of a given dimension is determined for each of a set of feature vectors that include at least one of the set of training samples and at least one of the set of test samples. A second centered Gram matrix of the given dimension is determined for a target value vector that includes target values from the set of training samples. A set of columns and rows associated with the at least one of the test samples in the second centered Gram matrix is set to 0. A subset of features is selected from a set of features based on the first and second centered Gram matrices. | 07-24-2014 |
20140207711 | TRANSDUCTIVE FEATURE SELECTION WITH MAXIMUM-RELEVANCY AND MINIMUM-REDUNDANCY CRITERIA - Various embodiments select features from a feature space. In one embodiment, a set of training samples and a set of test samples are received. The set of training samples includes a set of features and a class value. The set of test samples includes the set of features absent the class value. A relevancy with respect to the class value is determined for each of a plurality of unselected features based on the set of training samples. A redundancy with respect to one or more of the set of features is determined for each of the plurality of unselected features in the first set of features based on the set of training samples and the set of test samples. A set of features is selected from the plurality of unselected features based on the relevancy and the redundancy determined for each of the plurality of unselected features. | 07-24-2014 |
20140207712 | Classifying Based on Extracted Information - Information may be extracted from a document. A new pattern may be identified in the document. Classification may be performed based on the extracted information. | 07-24-2014 |
20140207713 | TRANSDUCTIVE FEATURE SELECTION WITH MAXIMUM-RELEVANCY AND MINIMUM-REDUNDANCY CRITERIA - Various embodiments select features from a feature space. In one embodiment, a set of training samples and a set of test samples are received. The set of training samples includes a set of features and a class value. The set of test samples includes the set of features absent the class value. A relevancy with respect to the class value is determined for each of a plurality of unselected features based on the set of training samples. A redundancy with respect to one or more of the set of features is determined for each of the plurality of unselected features in the first set of features based on the set of training samples and the set of test samples. A set of features is selected from the plurality of unselected features based on the relevancy and the redundancy determined for each of the plurality of unselected features. | 07-24-2014 |
20140207714 | TRANSDUCTIVE LASSO FOR HIGH-DIMENSIONAL DATA REGRESSION PROBLEMS - Various embodiments select features from a feature space. In one embodiment, a set of training samples and a set of test samples are received. A first centered Gram matrix of a given dimension is determined for each of a set of feature vectors that include at least one of the set of training samples and at least one of the set of test samples. A second centered Gram matrix of the given dimension is determined for a target value vector that includes target values from the set of training samples. A set of columns and rows associated with the at least one of the test samples in the second centered Gram matrix is set to 0. A subset of features is selected from a set of features based on the first and second centered Gram matrices. | 07-24-2014 |
20140207715 | DATA DRIVEN REDUCTION OF MULTI-SCALE MODELS - A method of computing physiological measurements resulting from a multi-scale physiological system using a data-driven model includes generating a database of physiological measurements associated with a multi-scale physiological system. A computer uses dimensionality reduction techniques on the database to identify a reduced set of components explaining the multi-scale physiological system. The computer learns a data-driven model of the multi-scale physiological system from the database. Then, new input parameters are received by the computer and used to compute new physiological measurements using the data-driven model. New derived physiological indicators are computed by the computer based on the reduced set of components. Once computed, the new derived physiological indicators may be displayed along with the new physiological measurements. | 07-24-2014 |
20140207716 | NATURAL LANGUAGE PROCESSING METHOD AND SYSTEM - A method, system and non-transitory computer-readable medium are provided for improving a statistical classification system, such as a statistical classification system that accepts natural language voice queries as inputs. A clustering engine may create one or more clusters of queries where the queries in each cluster are related in some way. A reviewing module may be employed to determine whether each cluster relates to an existing category supported by the classification system, a new category that can be supported by the classification system by training statistical models with the data from the cluster, is ambiguous, or is not useful to improve the classification system. For clusters determined to be useful for improving the system, the data in the clusters may be added to an existing training set or used as a training set to train new statistical models. | 07-24-2014 |
20140207717 | DATA CLASSIFICATION USING MACHINE LEARNING TECHNIQUES - Systems, methods and computer program products for classifying documents are presented. Systems, methods and computer program products for analyzing documents, e.g. for verifying an association of an invoice with an entity are also presented. Systems, methods and computer program products for managing medical records are presented. One exemplary system includes a memory; and a processor in communication with the memory, the processor being configured to process at least some instructions stored in the memory. The memory stores computer executable program code comprising instructions for: training a classifier based on an invoice format associated with a first entity; accessing a plurality of invoices labeled as being associated with at least one of the first entity and other entities; and outputting an identifier of at least one of the invoices having a high probability of not being associated with the first entity. | 07-24-2014 |
20140207718 | METHOD AND APPARATUS FOR IDENTIFYING USERS FROM RATING PATTERNS - Disclosed are methods and apparatus for identifying users of content. The methods include identifying contextual information of a group of users, gathering user access data of the users on the basis of the contextual information of the group of users, analyzing temporal information of the user access data, and identifying particular users in the group of users on the basis of the analyzed temporal information and the contextual information. | 07-24-2014 |
20140214732 | INCONSISTENCY DETECTION BETWEEN STRUCTURED AND NON-STRUCTURED DATA - A computer implemented method, computerized apparatus and computer program product for inconsistency detection between structured and non-structured data. The method comprising: automatically determining, by a computer, inconsistencies between fields in electronics records, the fields comprise at least a structured field and a non-structured field, the fields are designed to be able to include overlapping information in structured and non-structured form; and indicating, by the computer, to a user potential inconsistencies. Optionally, the indication uses a visual cue when displaying the electronic record to the user, wherein the visual cue indicates the fields which are determined to comprise inconsistent content. | 07-31-2014 |
20140214733 | Method And Apparatus For Deriving Diagnostic Data About A Technical System - A method and apparatus for deriving diagnostic data about a technical system utilizing learning metrics gained by at least one data driven learning process while generating and updating soft sensor models of said technical system. | 07-31-2014 |
20140214734 | CLASSIFYING A SUBMISSION - A technique includes receiving a submission classified by a plurality of human classifiers. Based at least in part on a classification model for the plurality of human classifiers and classification decisions made by the human classifiers, the submission is classified. | 07-31-2014 |
20140214735 | METHOD FOR AN OPTIMIZING PREDICTIVE MODEL USING GRADIENT DESCENT AND CONJUGATE RESIDUALS - An optimization in machine learning is achieved using Newton's algorithm together with an efficient technique for solving linear equations, such as the method of conjugate residuals. The techniques of the present invention are applicable to learning language models, predicting classes of objects from images and videos, and classifying financial transactions for prevention of fraud. Other uses include determining a function from a sequence of words to a relevant web page for a search engine, or to inverting arbitrary output values of an analyzed system into an internally running simulation. | 07-31-2014 |
20140214736 | TRAINING ENSEMBLES OF RANDOMIZED DECISION TREES - A method training a randomized decision tree through multiple iterations, each is based on:
| 07-31-2014 |
20140214737 | Selecting Social Networking System User Information for Display Via a Timeline Interface - The invention provides a display interface in a social networking system that enables the presentation of information related to a user in a timeline or map view. The system accesses information about a user of a social networking system, including both data about the user and social network activities related to the user. The system then selects one or more of these pieces of data and/or activities from a certain time period and gathers them into timeline units based on their relatedness and their relevance to users. These timeline units are ranked by relevance to the user, and are used to generate a timeline or map view for the user containing visual representations of the timeline units organized by location or time. The timeline or map view is then provided to other users of the social networking system that wish to view information about the user. | 07-31-2014 |
20140222722 | Adaptive system for continuous improvement of data - Adaptive system and process for improvement of data. A first rules module applies one or more data accuracy rules to a data input to improve data accuracy of the input. A second rules module applies one or more meta rules while applying data accuracy rules, the one or more meta rules invoking another event to improve data accuracy. | 08-07-2014 |
20140222723 | NAVIGATION SYSTEM WITH ANOMALY DETECTION MECHANISM AND METHOD OF OPERATION THEREOF - A method of operation of a navigation system includes: determining a classification model for a target field for assessing a point of interest; determining a cluster for the target field for assessing the point of interest; and determining an anomaly based on the classification model and the cluster for displaying on a device. | 08-07-2014 |
20140222724 | GENERATION OF LOG-LINEAR MODELS USING L-1 REGULARIZATION - A log-linear model may be trained using a modified version of an original limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm. The modified version may be based on modifying the original L-BFGS algorithm using a single map-reduce implementation. In another aspect, a sparse log-linear model may be accessed. The sparse log-linear model may be trained with L1-regularization, based on data indicating past user ad selection behaviors. A probability of a user selection of an ad may be determined based on the sparse log-linear model. | 08-07-2014 |
20140222725 | FAST LEARNING TO TRAIN LEARNING MACHINES USING SHADOW JOINING - In one embodiment, a node receives a request to initiate a shadow joining operation to shadow join a field area router (FAR) of a computer network, and preserves its data structures and soft states. The shadow joining operation may then be initiated to shadow join the FAR, wherein shadow joining comprises preforming join operations without leaving a currently joined-FAR, and the node measures one or more joining metrics of the shadow joining operation, and reports them accordingly. In another embodiment, a FAR (or other management device) determines a set of nodes to participate in a shadow joining operation, and informs the set of nodes of the shadow joining operation to shadow join the FAR. The device (e.g., FAR) participates in the shadow joining operation, and receives reports of one or more joining metrics of the shadow joining operation measured by the set of nodes. | 08-07-2014 |
20140222726 | ACCELERATING LEARNING BY SHARING INFORMATION BETWEEN MULTIPLE LEARNING MACHINES - In one embodiment, variables maintained by each of a plurality of Learning Machines (LMs) are determined. The LMs are hosted on a plurality of Field Area Routers (FARs) in a network, and the variables are sharable between the FARs. A plurality of correlation values defining a correlation between the variables is calculated. Then, a cluster of FARs is computed based on the plurality of correlation values, such that the clustered FARs are associated with correlated variables, and the cluster allows the clustered FARs to share their respective variables. | 08-07-2014 |
20140222727 | ENHANCING THE RELIABILITY OF LEARNING MACHINES IN COMPUTER NETWORKS - In one embodiment, network data is processed using a Learning Machine (LM) algorithm in a network, and results of the processing of network data are determined. A reliability checking algorithm is selected to determine a reliability level of the results. The reliability checking algorithm may be a local reliability checking algorithm or an external reliability checking algorithm. The reliability level of the results is determined using the reliability checking algorithm. Then, the LM algorithm is adjusted based on the determined reliability level. | 08-07-2014 |
20140222728 | TRIGGERING ON-THE-FLY REQUESTS FOR SUPERVISED LEARNING OF LEARNING MACHINES - In one embodiment, network data is received at a Learning Machine (LM) in a network. It is determined whether the LM recognizes the received network data based on information available to the LM. When the LM fails to recognize the received network data: a connection to a central management node is established, a request is sent for information relating to the unrecognized network data to the central management node, and information is received from the central management node in response to the request. The received information assists the LM in recognizing the unrecognized network data. | 08-07-2014 |
20140222729 | PRE-PROCESSING FRAMEWORK COMPONENT OF DISTRIBUTED INTELLIGENCE ARCHITECTURES - In one embodiment, a state tracking engine (STE) defines one or more classes of elements that can be tracked in a network. A set of elements to track is determined from the one or more classes, and the set of elements is tracked in the network. Access to the tracked set of elements then provided via one or more corresponding application programming interfaces (APIs). In another embodiment, a metric computation engine (MCE) defines one or more network metrics to be tracked in the network. One or more tracked elements are received from the STE. The one or more network metrics are tracked in the network based on the received one or more tracked elements. Access to the tracked network metrics is then provided via one or more corresponding APIs. | 08-07-2014 |
20140222730 | DISTRIBUTED ARCHITECTURE FOR MACHINE LEARNING BASED COMPUTATION USING A DECISION CONTROL POINT - In one embodiment, a request is received from a requesting node in a network to assist in distributing a task of the requesting node. Upon receiving the message, a capability to perform the task of one or more helping nodes in the network is evaluated, and a helping node of the one or more helping nodes is selected to perform the task based on the evaluated capability of the selected helping node. The distribution of the task is then authorized from the requesting node to the selected helping node. | 08-07-2014 |
20140222731 | DISTRIBUTED ARCHITECTURE FOR LAYERED HIDDEN MARKOV MODELS - In one embodiment, techniques are shown and described relating to a distributed architecture for layered Hidden Markov Models. In particular, in one embodiment, a Hidden Markov Model (HMM) at a layer i receives a sequence of hidden state produced by an HMM at a layer i−1, and uses the sequence of hidden state produced by the HMM at layer i−1 as input to the HMM at layer i, where the HMM at layer i−1 uses first time period bins, and the HMM at layer i uses second time period bins that are greater in length than the first time period bins. In another embodiment, the HMM at layer i originates the input (e.g., from measured properties), and produces the sequence of hidden state to output it to an HMM at a layer i+1 for use as its input. | 08-07-2014 |
20140222732 | MANAGING EDUCATIONAL CONTENT BASED ON DETECTED STRESS STATE - The methods and systems described herein may involve determining at least one lifeotype of at least one individual, analyzing the at least one lifeotype, and delivering content to at least one individual based on the analysis. The methods and systems described herein may involve providing a game, determining at least one lifeotype of at least one player of the game, analyzing the at least one lifeotype, and affecting the game play based on the analysis. The methods and systems described herein may involve providing an interactive space, determining at least one lifeotype of at least one individual in the space, analyzing the at least one lifeotype, and modifying at least one attribute of the space based on the analysis. | 08-07-2014 |
20140222733 | CONTROLLING A SENSORY DEVICE TO REDUCE STRESS BASED ON THE DETERMINED STRESS-RELATED STATE - The methods and systems described herein may involve determining at least one lifeotype of at least one individual, analyzing the at least one lifeotype, and delivering content to at least one individual based on the analysis. The methods and systems described herein may involve providing a game, determining at least one lifeotype of at least one player of the game, analyzing the at least one lifeotype, and affecting the game play based on the analysis. The methods and systems described herein may involve providing an interactive space, determining at least one lifeotype of at least one individual in the space, analyzing the at least one lifeotype, and modifying at least one attribute of the space based on the analysis. | 08-07-2014 |
20140222734 | CONTROLLING A SENSORY DEVICE BASED ON THE INFERRED STATE INFORMATION - The methods and systems described herein may involve determining at least one lifeotype of at least one individual, analyzing the at least one lifeotype, and delivering content to at least one individual based on the analysis. The methods and systems described herein may involve providing a game, determining at least one lifeotype of at least one player of the game, analyzing the at least one lifeotype, and affecting the game play based on the analysis. The methods and systems described herein may involve providing an interactive space, determining at least one lifeotype of at least one individual in the space, analyzing the at least one lifeotype, and modifying at least one attribute of the space based on the analysis. | 08-07-2014 |
20140222735 | SYSTEMS, METHODS, AND DEVICES TO DETERMINE AN INDIVIDUALS MOOD - The methods and systems described herein may involve determining at least one lifeotype of at least one individual, analyzing the at least one lifeotype, and delivering content to at least one individual based on the analysis. The methods and systems described herein may involve providing a game, determining at least one lifeotype of at least one player of the game, analyzing the at least one lifeotype, and affecting the game play based on the analysis. The methods and systems described herein may involve providing an interactive space, determining at least one lifeotype of at least one individual in the space, analyzing the at least one lifeotype, and modifying at least one attribute of the space based on the analysis. | 08-07-2014 |
20140222736 | Collaborative Analytics Map Reduction Classification Learning Systems and Methods - Disclosed herein are systems and methods for data learning and classification for rapidly processing extremely large volumes of input data using one or more computing devices, that are application and platform independent, participating in a distributed parallel processing environment. In one embodiments, a system may comprise a plurality of parallel Map Reduction Aggregation Processors operating on the one or more computing devices, and configured to receive different sets of input data for data aggregation. Each of the Map Reduction Aggregation Processors may comprise one or more parallel Mapping Operation Modules configured to consistently dissect the input data into individual intermediate units of mapping outputs comprising consistently mapped data keys, and any values related to mapped data keys, conducive to simultaneous parallel reduction processing; and one or more parallel Reduction Operation Modules configured to continually and simultaneously consume the mapping outputs by eliminating the matching keys and aggregating values consistent with a specified reduction operation for all matching keys that are encountered during consumption of the mapping outputs. The system may also include an application-specific Classification Metric Function Operations Module operating on the one or more computing devices and configured to receive reduction outputs from the Reduction Operations Modules to determine distance and/or similarity between each of the different sets of input data with respect to one or more data classification categories using one or more distance and/or similarity calculations. | 08-07-2014 |
20140222737 | System and Method for Developing Proxy Models - A system and method for developing proxy models is provided. The system for developing proxy models comprising a proxy model development computer system in electronic communication with a training database storing training data therein, and a plurality of computer models including a complex model and a proxy model that are trained by the computer system using the training data from the training database, wherein the computer system evaluates performance of each of the plurality of computer models, and if the computer system determines that the proxy model at least meets pre-defined performance criteria and approximates performance of the complex model, then the computer system communicates to a user that the proxy model can substitute the complex model. | 08-07-2014 |
20140236871 | SPARSE VARIABLE OPTIMIZATION DEVICE, SPARSE VARIABLE OPTIMIZATION METHOD, AND SPARSE VARIABLE OPTIMIZATION PROGRAM - A gradient computation unit computes a gradient of an objective function in a variable to be optimized. An added variable selection unit adds a variable corresponding to a largest absolute value of the computed gradient from among variables included in a variable set, to a nonzero variable set. A variable optimization unit optimizes a value of the variable to be optimized, for each variable included in the nonzero variable set. A deleted variable selection unit deletes a variable that, when deleted, causes a smallest increase of the objective function from among variables included in the nonzero variable set, from the nonzero variable set. An objective function evaluation unit computes a value of the objective function for the variable to be optimized. | 08-21-2014 |
20140236872 | METHOD FOR INTEGRATING AND FUSING HETEROGENEOUS DATA TYPES TO PERFORM PREDICTIVE ANALYSIS - A method and system for predicting the onset of a disease is provided. According to one example, the method includes receiving patient data including a first input sample of a first data type and a second input sample of a second data type, the first data type including discrete data and the second data type including continuous data, receiving a training data set including a first plurality of training samples of the first data type and a corresponding second plurality of training samples of the second data type, providing the first input sample and the first plurality of training samples to a first kernel function of a multiple kernel decision function, providing the second input sample and the second plurality of training samples to a second kernel function of the multiple kernel decision function, performing at least one calculation using the multiple kernel decision function to produce at least one result, and determining a probability of whether the patient data indicates that the patient will develop the disease based on the at least one result of the multiple kernel decision function. | 08-21-2014 |
20140236873 | PACKET PREDICTION IN A MULTI-PROTOCOL LABEL SWITCHING NETWORK USING OPERATION, ADMINISTRATION, AND MAINTENANCE (OAM) MESSAGING - A first switch in a MPLS network receives a plurality of packets that are part of a pair of flows. The first switch performs a packet prediction learning algorithm on the first plurality of packets and generates packet prediction information that is communicated to a second switch within the MPLS network utilizing an Operations, Administration, and Maintenance (OAM) packet (message). In a first example, the first switch communicates a packet prediction information notification to a Network Operations Center (NOC) that in response communicates a packet prediction control signal to the second switch. In a second example, the first switch does not communicate a packet prediction information notification. In the first example, the second switch utilizes the packet prediction control signal to determine if the packet prediction information is to be utilized. In the second example, second switch independently determines if the packet prediction information is to be used. | 08-21-2014 |
20140236874 | BINARY CLASSIFICATION OF ITEMS OF INTEREST IN A REPEATABLE PROCESS - A system includes host and learning machines. Each machine has a processor in electrical communication with at least one sensor. Instructions for predicting a binary quality status of an item of interest during a repeatable process are recorded in memory. The binary quality status includes passing and failing binary classes. The learning machine receives signals from the at least one sensor and identifies candidate features. Features are extracted from the candidate features, each more predictive of the binary quality status. The extracted features are mapped to a dimensional space having a number of dimensions proportional to the number of extracted features. The dimensional space includes most of the passing class and excludes at least 90 percent of the failing class. Received signals are compared to the boundaries of the recorded dimensional space to predict, in real time, the binary quality status of a subsequent item of interest. | 08-21-2014 |
20140236875 | MACHINE LEARNING FOR REAL-TIME ADAPTIVE WEBSITE INTERACTION - Apparatuses, systems, methods, and computer program products are disclosed for website interaction. An input module is configured to receive information from multiple sources. The information may be associated with a user of a website. A machine learning module is configured to apply machine learning to the information to produce a machine learning result. A website adaptation module is configured to adapt the website for the user in real-time based on the machine learning result. | 08-21-2014 |
20140244548 | SYSTEM, METHOD, AND COMPUTER PROGRAM PRODUCT FOR CLASSIFICATION OF SILICON WAFERS USING RADIAL SUPPORT VECTOR MACHINES TO PROCESS RING OSCILLATOR PARAMETRIC DATA - A system, method, and computer program product for testing and classifying silicon wafers using a support vector machine. The method includes the steps of receiving parametric data associated with one or more die on a wafer and analyzing the parametric data via a support vector machine to determine a classification for each die of the one or more die. The parametric data includes at least one ring oscillator ratio. The method further includes the step of determining a classification of the wafer based on the classification of the one or more die. | 08-28-2014 |
20140244549 | Predictive Cueing - A method and system that provide for decision support and/or training support in crisis decision-making situations are provided. In one implementation, for example, a method identifies patterns from known cases based on information from a crisis event. Each of the known cases includes attributes and at least one outcome. The method also identifies a first subset of the known cases that relate to the identified patterns from the known cases. The method also analyzes the identified patterns to determine a cue that, if answered, will provide a second subset of the known cases including a more converged range of decision outcomes than the first subset. | 08-28-2014 |
20140244550 | POSTERIOR PROBABILITY PURSUIT FOR ENTITY DISAMBIGUATION - Various technologies described herein pertain to disambiguation of a mention of an ambiguous entity in a document. A set of candidate entities can be retrieved from an entity knowledge base based upon the mention of the ambiguous entity, where each of the candidate entities has a respective entity feature representation. Moreover, a document feature representation can be generated based upon features of the document and the respective entity feature representations of the candidate entities. A processor can be caused to select a subset of features from the document feature representation based upon a measure of how discriminative the features from the document feature representation are for disambiguating the mention of the ambiguous entity. A disambiguated result for the mention of the ambiguous entity can be determined based upon the subset of the features. The disambiguated result can be an unknown entity or one of the candidate entities. | 08-28-2014 |
20140244551 | INFORMATION SPREAD SCALE PREDICTION DEVICE, INFORMATION SPREAD SCALE PREDICTION METHOD, AND INFORMATION SPREAD SCALE PREDICTION PROGRAM - To provide an information spread scale prediction device capable of accurately predicting the number of future contributions for a specific topic in SNS and the like. The information spread scale prediction device includes: a learning text data input unit which acquires learning text data from a specific website; a node influence learning unit which calculates the influence for the number of statements by each group to which a node specifying a single specific user belongs for the topic from the number of statements by each classified topic, and stores it as learning data; a prediction text data input unit which acquires prediction text data from the specific website after storing the learning data; and a future contribution number prediction unit which predicts and outputs the number of contributions at a specific future time of the topic based on the number of statements of each topic and the learning data. | 08-28-2014 |
20140244552 | GLOBAL MODEL FOR FAILURE PREDICTION FOR ARTIFICIAL LIFT SYSTEMS - Methods and systems for predicting failures in an artificial lift system are disclosed. One method includes extracting one or more features from a dataset including time sampled performance of a plurality of artificial lift systems disposed across a plurality of different oil fields, the dataset including data from failed and normally operating artificial lift systems. The method also includes forming a learning model based on identified pre-failure signatures in the extracted features, the learning model configured to predict a failure of an artificial lift system based on observation of one of the identified pre-failure signatures in operational data received from the artificial lift system. | 08-28-2014 |
20140244553 | MACHINE LEARNING APPARATUS AND MACHINE LEARNING METHOD - A machine learning apparatus includes an analytical information storage unit that stores therein two or more pieces of analytical information each associating input/output information used for machine learning with time-point information indicating a time point of the input/output information, an analysis-object-set specifying unit that specifies an analysis object set containing a unit-period input-output set being a set of pieces of the input/output information corresponding to pieces of the time-point information indicating the time point in a unit period and an amount of the pieces of the input/output information of the set being dependent on a period between the time point of the unit periods and a specific time point, and a machine learning unit that performs machine learning by using the pieces of the input/output information contained in the analysis object set. | 08-28-2014 |
20140244554 | NON-DETERMINISTIC FINITE STATE MACHINE MODULE FOR USE IN A REGULAR EXPRESSION MATCHING SYSTEM - A non-deterministic finite state machine module for use in a regular expression matching system. The system includes a computational unit implementing a non-deterministic finite state machine representing a regular expression, wherein the computational unit is configured to: receive an input data stream, wherein an occurrence of the regular expression is determined, and an activation signal; process the input data stream with respect to the non-deterministic finite state machine depending on the activation signal; and provide at least one branch data output for initializing an additional non-deterministic finite state machine module if the processing of an element of the input data stream according to the non-deterministic finite state machine results in a branching of a processing thread. | 08-28-2014 |
20140244555 | Method And Apparatus For A Predictive Tracking Device - A predictive tracking method and apparatus utilizing objective and subjective data in order to predict user states is provided herein. For example, some such embodiments may allow a user to track their mood or health symptoms in relation to retrieved data regarding their environmental in order to reveal patterns that can help forecast and proactively manage mood or health symptoms. | 08-28-2014 |
20140250032 | METHODS, SYSTEMS AND PROCESSOR-READABLE MEDIA FOR SIMULTANEOUS SENTIMENT ANALYSIS AND TOPIC CLASSIFICATION WITH MULTIPLE LABELS - Methods, systems and processor-readable media for simultaneous sentiment analysis and topic classification with multiple labels. A sentiment and topic associated with a post can be classified at similar time and a result can be incorporated to predict a feature so that a label of two (or more) tasks can promote and reinforce each other iteratively. A feature extraction and selection can be performed on the tasks and a multi-task multi-label classification model can be trained for each task with maximum entropy utilizing multiple labels to ascertain information derived from an extra label and to manage class ambiguities. Each task has a separate classification model with different predicting features and they can be trained collectively which allows flexibility in model construction. The multi-task multi-label classification model produces a probabilistic result and the classes can be ranked by the probabilistic result and the post can be classified with the multi-label. | 09-04-2014 |
20140250033 | SOCIAL BEHAVIOR HYPOTHESIS TESTING - A relational event history is determined based on a data set, the relational event history including a set of relational events that occurred in time among a set of actors. Data is populated in a probability model based on the relational event history, where the probability model is formulated as a series of conditional probabilities that correspond to a set of sequential decisions by an actor for each relational event, and where the probability model includes one or more statistical parameters and corresponding statistics. A baseline communications behavior for the relational event history is determined based on the populated probability model, and departures from the baseline communications behavior within the relational event history are determined. Determining departures includes determining and testing a hypothesis regarding communications behavior within the relational event history. | 09-04-2014 |
20140250034 | METHOD AND APPARATUS FOR IMPROVING RESILIENCE IN CUSTOMIZED PROGRAM LEARNING NETWORK COMPUTATIONAL ENVIRONMENTS - An apparatus and a method are provided for learning a program with a large number of parameters. In one embodiment, a method not only distorts the input values, but also distorts some of the parameters in the program model. Such an approach not only forces the learned program to acquire parameter values to predict missing or desired data, but also to correct errors in the input data and the program parameters themselves, thereby rendering the learned program more resilient to overfitting and falling into local optima. | 09-04-2014 |
20140250035 | MODEL GENERATION DEVICE, PATTERN RECOGNITION APPARATUS AND METHODS THEREOF - One aspect of the embodiments discloses a model generation device for pattern recognition, a pattern recognition apparatus and methods thereof. A mixture-level variance sharing step generates a mixture-level variance sharing structure of a first model by using a second model. A first model generation step generates the first model with the variance sharing structure by using training data of the first model, wherein in the variance sharing structure, mixture components in respective states have the same shared variances in the same order. The embodiment can at least provide better model parameter estimation so as to provide better recognition performance in the case of limited training data. | 09-04-2014 |
20140258185 | METHOD FOR REVEALING A TYPE OF DATA - A method for revealing a type of data is provided herein. The method is comprising
| 09-11-2014 |
20140258186 | Generic Programming for Diagnostic Models - A system for compiling a machine operable diagnostic system includes a header unit, a component unit, an effects unit, a test unit, and an instantiation unit. The header unit identifies general properties of a system from a generic description in an electronic format which includes at least one category of a plurality of variations. The component unit identifies a plurality of components of a modeled system from the generic description, and adds and separates the plurality of variations to the plurality of components, and each identified component includes at least one type of failure and a probability of the at least one type of failure. The effects unit identifies from the generic description a plurality of observable effects, each observable effect corresponding to at least one type of failure for at least one component. The test unit identifies from the generic description a plurality of tests and test outcomes designed to elicit the observable effects. The instantiation unit constructs a procedural instantiation of the machine operable diagnostic system based on the general properties, the identified components, the identified observable effects, and the identified tests and test outcomes. | 09-11-2014 |
20140258187 | GENERATING DATABASE CLUSTER HEALTH ALERTS USING MACHINE LEARNING - A method, system, and computer program product for generating database cluster health alerts using machine learning. A first database cluster known to be operating normally is measured and modeled using machine learning techniques. A second database cluster is measured and compared to the learned model. More specifically, the method collects a first set of empirically-measured variables of a first database cluster, and using the first set of empirically-measured variables a mathematical behavior predictor model is generated. Then, after collecting a second set of empirically-measured variables of a second database cluster over a plurality of second time periods, the mathematical behavior predictor model classifies the observed behavior. The classified behavior might be deemed to be normal behavior, or some form of abnormal behavior. The method forms and report alerts when the classification deemed to be anomalous behavior, or fault behavior. A Bayesian belief network predicts the likelihood of continued anomalous behavior. | 09-11-2014 |
20140258188 | PREDICTING USER ACTIVITY IN SOCIAL MEDIA APPLICATIONS - Embodiments of the invention relate to predicting user activity in a social media application. In one embodiment, user activity information is collected for a user from a social media application and activity features of the user are determined, based on the collected activity information for the user. Then a model is created to predict a future activity of the user in the social media application. The model uses the determined activity features of the user and results obtained from running the created model, to determine future activity of the user in the social media application. | 09-11-2014 |
20140258189 | SYSTEM AND METHOD FOR AUTO-QUERY GENERATION - Various systems and methods provide an intuitive user interface that enables automatic specification of queries and constraints for analysis by ML component. Various implementations provide methodologies for automatically formulating machine learning (“ML”) and optimization queries. The automatic generation of ML and/or optimization queries can be configured to use examples to facilitate formulation of ML and optimization queries. One example method includes accepting input data specifying variables and data values associated with the variables. Within the input data any unspecified data records are identified, and a relationship between the variables specified in the input data and a variable associated with the at least one unspecified data record is automatically determined. The relationship can be automatically determined based on training data contained within the input data. Once a relationship is established a ML problem can be automatically generated. | 09-11-2014 |
20140258190 | Method and System for Providing Contextual Based Medication Dosage Determination - Methods and devices for statistical determination of medication dosage level such as bolus amount based on contextual information are provided. | 09-11-2014 |
20140258191 | ADAPTIVE RANKING OF NEWS FEED IN SOCIAL NETWORKING SYSTEMS - Machine learning models are used for ranking news feed stories presented to users of a social networking system. The social networking system divides its users into different sets, for example, based on demographic characteristics of the users and generates one model for each set of users. The models are periodically retrained. The news feed ranking model may rank news feeds for a user based on information describing other users connected to the user in the social networking system. Information describing other users connected to the user includes interactions of the other users with objects associated with news feed stories. These interactions include commenting on a news feed story, liking a news feed story, or retrieving information, for example, images, videos associated with a news feed story. | 09-11-2014 |
20140258192 | APPARATUS FOR TRAINING RECOGNITION CAPABILITY USING ROBOT AND METHOD FOR SAME - Disclosed is an apparatus for training a recognition capability by using a robot and to a method for same. Disclosed is the apparatus for training the recognition capability, according to one embodiment of the present invention, comprising: an instruction generation portion for transmitting to the robot a series of robot instructions for controlling the behavior of the robot; a sensor portion for collecting sensor information including a 3D position information and color information of a trainee; a trainee behavioral information generation portion for generating behavioral information of the trainee, based on the sensor information that is collected; and a recognition capability determination portion for outputting the recognition capability of the trainee, based on the robot instructions and the behavioral information of the trainee. | 09-11-2014 |
20140279729 | METHODS AND APPARATUS FOR ENTITY DETECTION - Techniques for entity detection include matching a token from at least a portion of a text string with a matching concept in an ontology, wherein the at least a portion of the text string has been labeled as corresponding to a particular entity type. A first concept may be identified as being hierarchically related to the matching concept within the ontology, and a second concept may be identified as being hierarchically related to the first concept within the ontology. Based at least in part on the labeling of the at least a portion of the text string as corresponding to the particular entity type, a statistical model may be trained to associate the first concept with a first probability of corresponding to the particular entity type and the second concept with a second probability of corresponding to the particular entity type. | 09-18-2014 |
20140279730 | IDENTIFYING SALIENT ITEMS IN DOCUMENTS - A set of representations of item-page pairs of items and respective web pages that include the respective items is obtained, each representation including feature function values indicating weights associated with features of associated web pages, the features including page classification features. An annotated set of labeled training data that is annotated with salience annotation values of items for respective web pages that include the items is obtained. The salience annotation values are determined based on a soft function, by determining a first count of a total number of user queries associated with corresponding visits to the respective web pages, and determining a ratio of a second count to the first count, the second count determined as a cardinality of a subset of the corresponding visits that are associated with user queries that include the item, the subset included in the corresponding visits. Models are trained using the annotated set. | 09-18-2014 |
20140279731 | System and Method for Automated Text Coverage of a Live Event Using Structured and Unstructured Data Sources - A system for providing text coverage of a live event, comprising one or more computing devices configured to receive information from one or more structured data sources and from one or more unstructured data sources, and to output information derived therefrom in a periodically updated timeline; a game data processing system comprising a system for deriving data and a story generation system; a social media processing system; and a data source mixing system. A detailed specific example of an embodiment is disclosed in which the live event is a basketball game. | 09-18-2014 |
20140279732 | System for categorizing lists of words of arbitrary origin - The present disclosure provides for categorization of lists of words. The method comprises querying DBpedia to find the resources related to the given list of words. Once the resources are found, the corresponding media Wikipedia categories can be retrieved, as well as their ancestors, generating a graph of categories. A number of graph analysis algorithms can then be applied to the graph, each returning a selected category. For each algorithm a classifier is trained to decide whether the output of the algorithm is indeed the “best” category. An ensemble weighted majority voting can then be used to select the best category based on votes cast by each classifier. The disclosure demonstrates a more accurate selection of the best category and can include an ensemble majority rated voting algorithm comprising all voting members initially casting one vote; i.e., highest frequency, most frequently occurring word, least common ancestor and centrality measures. | 09-18-2014 |
20140279733 | COMPUTER-BASED METHOD AND SYSTEM FOR PROVIDING ACTIVE AND AUTOMATIC PERSONAL ASSISTANCE USING A ROBOTIC DEVICE/PLATFORM - A method and a system for providing personal assistance in daily activities. A method and a system for actively and automatically providing personal assistance, using a robotic device/platform, based on detected data regarding a user and the user's environment. The method and system may include a processor, at least one sensor, an output device, a communications unit, and a database. The database may further include a memory and cloud-based database and computing. The method and system may provide assistance in jogging the memory of the user, parental control of a child, hazard detection, and various other circumstances. The method and system may actively and automatically provide personal assistance regarding health, exercise, diet, or nutrition. The method and system may assist the user or a health professional in health diagnosis and treatment. | 09-18-2014 |
20140279734 | Performing Cross-Validation Using Non-Randomly Selected Cases - A technique to perform cross-validation using a set of randomly selected labeled cases and a set of non-randomly selected labeled cases. A training set for use during cross-validation can include cases from both sets. A test set for use during cross-validation can include cases from the randomly selected set but exclude cases from the non-randomly selected set. | 09-18-2014 |
20140279735 | PROCESS MODEL GENERATED USING BIASED PROCESS MINING - Embodiments relate to a method, system, and computer program product for a process model. The method includes extracting data associated with a process execution trace of a running process and extracting any prior knowledge data relating to the running process. The method also includes calculating at least one transition confidence parameter for the prior knowledge data; and identifying any existing process models relating to the running process. A confidence trace bias is also generated for any existing process model identified. An enhanced bias value is then calculated by combining the confidence trace bias value and value of the transition confidence parameter. Using as input the extracted process execution trace data, the prior knowledge data, the identified existing model and the enhanced bias value, a learned process model is then generated. | 09-18-2014 |
20140279736 | METHOD AND SYSTEM FOR MAPPING SHORT TERM RANKING OPTIMIZATION OBJECTIVE TO LONG TERM ENGAGEMENT - Method, system, and programs for identifying a target metric. In one example, at least one first type of metric computed based on a first period associated with a first length of time is measured for each of a plurality of users. At least one second type of metric computed based on a second period associated with a second length of time is measured for each of the plurality of users. The second length of time is larger than the first length of time. Correlations between each of the at least one first type of metric and each of the at least one second type of metrics are computed with respect to the plurality of users. A target metric is identified from the at least one first type of metric based on the correlations. The target metric is correlated with the at least one second type of metric. | 09-18-2014 |
20140279737 | MONTE-CARLO APPROACH TO COMPUTING VALUE OF INFORMATION - The subject disclosure is directed towards the use of Monte Carlo (MC) procedures for computing the value of information (VOI), including with long evidential sequences. An MC-VOI algorithm is used to output a decision as to balancing the value and costs of collecting information in advance of taking action by running prediction model-based simulations to determine execution paths through possible states, and processing the results of the simulations/paths taken into a final decision. | 09-18-2014 |
20140279738 | Non-Linear Classification of Text Samples - Non-linear classifiers and dimension reduction techniques may be applied to text classification. Non-linear classifiers such as random forest, Nyström/Fisher, and others, may be used to determine criteria usable to classify text into one of a plurality of categories. Dimension reduction techniques may also be used to reduce feature space size. Machine learning techniques may be used to develop criteria (e.g., trained models) that can be used to automatically classify text. Automatic classification rates may be improved and result in fewer numbers of text samples being unclassifiable or being incorrectly classified. User-generated content may be classified, in some embodiments. | 09-18-2014 |
20140279739 | RESOLVING AND MERGING DUPLICATE RECORDS USING MACHINE LEARNING - According to various embodiments of the present invention, an automated technique is implemented for resolving and merging fields accurately and reliably, given a set of duplicated records that represents a same entity. In at least one embodiment, a system is implemented that uses a machine learning (ML) method, to train a model from training data, and to learn from users how to efficiently resolve and merge fields. In at least one embodiment, the method of the present invention builds feature vectors as input for its ML method. In at least one embodiment, the system and method of the present invention apply Hierarchical Based Sequencing (HBS) and/or Multiple Output Relaxation (MOR) models in resolving and merging fields. Training data for the ML method can come from any suitable source or combination of sources. | 09-18-2014 |
20140279740 | METHOD AND APPARATUS FOR DETECTION AND PREDICTION OF EVENTS BASED ON CHANGES IN BEHAVIOR - A computer-implemented process for detecting and predicting events occurring to a person, includes: observing, using a sensor, a reading of a parameter of a body part of the person which is one of: horizontal location, vertical height, orientation, velocity, and time of observation, wherein the reading corresponds to less information than needed to define the person's posture; receiving the reading into a computer memory; determining from the received reading a pattern of behavior; detecting a change in behavior; identifying from the change in behavior a combination of one or more readings corresponding to an abnormal event; and producing an alert signal when the combination of one or more readings corresponding to the abnormal event is identified. The process may be practiced using a computing machine including a computer memory; a sensor; and a computer processor. | 09-18-2014 |
20140279741 | SCALABLE ONLINE HIERARCHICAL META-LEARNING - A method of meta-learning includes receiving a prediction objective, extracting a plurality of subsets of data from a distributed dataset, generating a plurality of local predictions, wherein each local prediction is based on a different subset of the plurality of subsets of data and the prediction objective, combining the plurality of local predictions, and generating a final prediction based on the combined local predictions. | 09-18-2014 |
20140279742 | DETERMINING AN OBVERSE WEIGHT - A technique for determining an obverse weight. A set of cases can be divided into bins. An obverse weight for a bin can be determined based on an importance weight of the bin and a variance of an error estimate of the bin. | 09-18-2014 |
20140279743 | JABBA-TYPE OVERRIDE FOR CORRECTING OR IMPROVING OUTPUT OF A MODEL - Example methods, apparatuses, or articles of manufacture are disclosed that may be implemented, in whole or in part, using one or more computing devices to facilitate or otherwise support one or more processes or operations for a Jabba-type override for correcting or improving output of a model, such as a machine-learned model, for example. | 09-18-2014 |
20140279744 | CONTINUOUS INTERACTION LEARNING AND DETECTION IN REAL-TIME - Systems and methods may provide for partitioning a plurality of training samples into a first sequential list of centroids, removing one or more repeating centroids in the first sequential list of centroids to obtain a first reduced list of centroids and generating a set of Hidden Markov Model (HMM) parameters based on the first reduced list of centroids. Additionally, a plurality of detection samples may be partitioned into a second sequential list of centroids, wherein one or more repeating centroids in the second sequential list of centroids may be removed to obtain a second reduced list of centroids. The second reduced list of centroids may be used to determine a match probability for the plurality of detection samples against the set of HMM parameters. In one example, the reduced lists of centroids lack temporal variability. | 09-18-2014 |
20140279745 | CLASSIFICATION BASED ON PREDICTION OF ACCURACY OF MULTIPLE DATA MODELS - A dynamic classifier for performing binary classification of instance data using oracles that predict accuracy of predictions made by corresponding models. An oracle corresponding to a model is trained to generate a confidence value that represents accuracy of a prediction made by the model. Based on the confidence value and predictions, one of multiple models is selected and its prediction is used as an intermediate prediction. The intermediate prediction may be used in conjunction with another prediction generated using a different algorithm to generate a final prediction. By using the confidence value for each model, a more accurate prediction can be made. | 09-18-2014 |
20140279746 | EXPERT SYSTEM FOR DETERMINING PATIENT TREATMENT RESPONSE - A medical digital expert system to predict a patient's response to a variety of treatments (using pre-treatment information) is described. The system utilizes data fusion, advanced signal/information processing and machine learning/inference methodologies and technologies to integrate and explore diverse sets of attributes, parameters and information that are available to select the optimal treatment choice for an individual or for a subset of individuals suffering from any illness or disease including psychiatric, mental or neurological disorders and illnesses. The methodology and system can also be used to determine or confirm medical diagnosis, estimate the level, index, severity or critical medical parameters of the illness or condition, or provide a list of likely diagnoses for an individual suffering/experiencing any illness, disorder or condition. | 09-18-2014 |
20140279747 | System and Method for Model-based Inventory Management of a Communications System - A method for management entity operations includes receiving a request to collect data for an entity in a communications system, collecting the data for the entity utilizing a set of protocols selected using knowledge defined by a first data model of a data model list derived from an information model of the communications system, and saving the data collected. | 09-18-2014 |
20140279748 | METHOD AND PROGRAM STRUCTURE FOR MACHINE LEARNING - A method using a recognizer program structure is used in a program that is learned over training data. The method includes (a) for each vector in an input tuple of vectors, (i) mapping the vector to one of a domain index; (ii) using the domain index to select one or more corresponding linear transformations; (iii) applying one or more of the selected linear transformations on the vector to obtain a resulting vector in a first intermediate space; and (iv) applying a predetermined function on each element of the resulting vector to obtain an output vector in a second intermediate space; and (b) mapping the resulting vectors of the second intermediate space by linear transformation to obtain an output tuple of vectors in R | 09-18-2014 |
20140279749 | MECHANISM FOR FACILITATING IMPROVED SEARCHING - Improved integrated search techniques. A request for performance of a search for objects is received within a multi-tenant database environment having a plurality of tenants each having individual tenant information. A query is generated in response to the request. The query is specialized based on tenant information corresponding to a tenant from which the request originates. The tenant information is retrieved from the multi-tenant database environment. The query is performed on information stored in the multi-tenant database environment. Results of the query are presented to a user in a graphical user interface. | 09-18-2014 |
20140279750 | METHOD AND SYSTEM FOR IDENTIFYING A CLEAN ENDPOINT TIME FOR A CHAMBER - Systems and methods are provided for determining a clean endpoint time for a current run of a chamber. The clean endpoint time for the current run may be determined by determining that a chamber parameter, such as a chamber pressure, has stabilized. Historical clean endpoint time data is updated by adding the clean endpoint time for the current run of the chamber. A recommended clean endpoint time is then determined for the chamber based on the updated historical clean endpoint time data. | 09-18-2014 |
20140279751 | AGGREGATION AND ANALYSIS OF MEDIA CONTENT INFORMATION - Disclosed are the method and apparatus for collecting and analyzing media content metadata. The technology retrieves web documents referencing media objects from web servers. Metadata of the media objects such as global tags and category weight values are generated from the web documents. Affinity values between user identities and the media objects are generated based on online behaviors of the users interacting with the media objects. Based on the affinity values and metadata of the media objects, the technology can provide recommendations of media objects. | 09-18-2014 |
20140279752 | System and Method for Generating Ultimate Reason Codes for Computer Models - A system and method for generating ultimate reason codes for computer models is provided. The system for generating ultimate reason codes for computer models comprising a computer system for receiving a data set, and an ultimate reason code generation engine stored on the computer system which, when executed by the computer system, causes the computer system to train a base model with a plurality of reason codes, wherein each reason code includes one or more variables, each of which belongs to only one reason code, train a subsequent model using a subset of the plurality of reason codes, determine whether a high score exists in the base model, determine a scored difference if a high score exists in the base model, and designate a reason code having a largest drop of score as an ultimate reason code. | 09-18-2014 |
20140279753 | METHODS AND SYSTEM FOR PROVIDING SIMULTANEOUS MULTI-TASK ENSEMBLE LEARNING - A complete end-to-end modeling system is provided that includes data sampling, feature engineering, action labeling, and model learning or learning from models built based on collected data. The end-to-end modeling process is performed via an automatic mechanism with minimal or reduced human intervention. A processor-readable medium is disclosed, storing processor-executable instructions to instantiate an automated data sampling and prediction structure training component, the automated data sampling and prediction structure training component being configured to automatically collect user event data samples, and use the collected user event data samples to train multiple prediction structures in parallel. | 09-18-2014 |
20140279754 | SELF-EVOLVING PREDICTIVE MODEL - Systems and methods are provided for predicting clinical parameters. A model of a plurality of models having a sufficient accuracy, given a received set of predictors, is selected. A value for a clinical parameter is predicted from the selected model and the set of predictors to provide a predicted value. A value for the clinical parameter is measured, and the model is updated according to the set of predictors and the measured value. | 09-18-2014 |
20140279755 | MANIFOLD-AWARE RANKING KERNEL FOR INFORMATION RETRIEVAL - A manifold-aware ranking kernel (MARK) for information retrieval is described herein. The MARK is implemented by using supervised and unsupervised learning. MARK is ranking-oriented such that the relative comparison formulation directly targets on the ranking problem, making the approach optimal for information retrieval. MARK is also manifold-aware such that the algorithm is able to exploit information from ample unlabeled data, which helps to improve generalization performance, particularly when there are limited number of labeled constraints. MARK is nonlinear: as a kernel-based approach, the algorithm is able to lead to a highly non-linear metric which is able to model complicated data distribution. | 09-18-2014 |
20140279756 | CROSS MEDIA RECOMMENDATION - Methods, systems and computer program products are provided for cross-media recommendation by store a plurality of taste profiles corresponding to a first domain and a plurality of media item vectors corresponding to a second domain. An evaluation taste profile in the first domain is applied to a plurality of models that have been generated based on relationship among the plurality of taste profiles and the plurality of media item vectors, and obtain a plurality of resulting codes corresponding to at least one of the plurality of media item vectors in the second domain. | 09-18-2014 |
20140279757 | APPARATUS, SYSTEMS, AND METHODS FOR GROUPING DATA RECORDS - The present application relates to apparatus, systems, and methods for grouping data records based on entities referenced by the data records. The disclosed grouping mechanism can include determining a pair-wise similarity between a large number of data records, and clustering a subset of the data records based on their pair-wise similarity. | 09-18-2014 |
20140279758 | COMPUTATIONAL METHOD FOR PREDICTING FUNCTIONAL SITES OF BIOLOGICAL MOLECULES - In a general aspect, a method for inferring one or more biomolecule-to-biomolecule interaction sites includes receiving data representative of a plurality of prediction models. Each prediction model is associated with a different atom type of a plurality of atom types and characterizes biomolecule-to-biomolecule interaction site specific patterns common to a plurality of three dimensional probability density maps. Each three dimensional probability density map is associated with a corresponding biomolecule of a plurality of biomolecules included in a training data set and represents a probability of a non-covalent interacting atom on a surface of the corresponding biomolecule interacting with the atom type associated with the prediction model. Data representative of a query biomolecule is received, the data including one or more unknown biomolecule-to-biomolecule interaction sites. The one or more unknown biomolecule-to-biomolecule interaction sites of the query biomolecule are inferred based on the data representative of the plurality of prediction models. | 09-18-2014 |
20140279759 | TRAINING OF STORAGE DEVICES IN COMPUTING SYSTEMS AND ENVIRONMENTS - Storage devices and components, including memory components (e.g., non-volatile memory) can be trained by executable code that facilitates and/or performs reads and/or write requests to one or more storage sub-modules of a storage component (e.g., memory configured on a memory channel) made up of multiple storage components (e.g., DIMMs). The executable code can also train multiple storage components at the same time and/or in parallel. | 09-18-2014 |
20140279760 | Data Analysis Computer System and Method For Conversion Of Predictive Models To Equivalent Ones - The present invention addresses two ubiquitous and pressing problems of modern data analytics technology. Many modern pattern recognition technologies produce models with excellent predictivity but (a) they are “black boxes”, that is they are opaque to the user; (b) they are too large, and/or expensive to execute in less powerful computing platforms. The invention “opens up” a black box model by converting it to a compact and understandable model that is functionally equivalent. The invention also converts a predictive model into a functionally equivalent model into a form that can be implemented and deployed more easily or efficiently in practice. The benefits include: model understandability and defensibility of modeling. A particularly interesting application is that of understanding the decision making of humans, comparison of the behavior of a human or computerized decision process against another and use to enhance education and guideline compliance/adherence detection and improvement. The invention can be applied to practically any field where predictive modeling (classification and regression) is desired because it relies on extremely broad distributional assumptions that are valid in numerous fields. | 09-18-2014 |
20140279761 | Document Coding Computer System and Method With Integrated Quality Assurance - The present invention consists of a computer-implemented system and method for automatically analyzing and coding documents into content categories suitable for high cost, high yield settings where quality and efficiency of classification are essential. A prototypical example application field is legal document predictive coding for purposes of e-discovery and litigation (or litigation readiness) where the automated classification of documents as “responsive” or not must be (a) efficient, (b) accurate, and (c) defensible in court. Many text classification technologies exist but they focus on the relatively simple steps of using a training method on training data, producing a model and testing it on test data. They invariably do not address effectively and simultaneously key quality assurance requirements. The invention applies several data design and validation steps that ensure quality and removal of all possible sources of document classification error or deficiencies. The invention employs multiple classification methods, preprocessing methods, visualization and organization of results, and explanation of models which further enhance predictive quality, but also ease of use of models and user acceptance. The invention can be applied to practically any field where text classification is desired. | 09-18-2014 |
20140279762 | ANALYTICAL NEURAL NETWORK INTELLIGENT INTERFACE MACHINE LEARNING METHOD AND SYSTEM - A learning framework and methods of machine learning are disclosed. Specifically, an Analytical Neural Network Intelligent Interface (ANNII) is disclosed that includes the ability to analyze incoming data in substantially real-time and determine whether or not the data is statistically anomalous data. Learning models can then be updated depending upon whether or not the data is determined to be statistically anomalous data or not. | 09-18-2014 |
20140279763 | System and Method for Automated Scoring of a Summary-Writing Task - In accordance with the teachings described herein, systems and methods are provided for measuring a user's comprehension of subject matter of a text. A summary generated by the user is received, where the summary summarizes the text. The summary is processed to determine a first numerical measure indicative of a similarity between the summary and a reference summary. The summary is processed to determine a second numerical measure indicative of a degree to which a single sentence of the summary summarizes an entirety of the text. The summary is processed to determine a third numerical measure indicative of a degree of copying in the summary of multi-word sequences present in the text. A numerical model is applied to the first numerical measure, the second numerical measure and the third numerical measure to determine a score for the summary indicative of the user's comprehension of the subject matter of the text. | 09-18-2014 |
20140279764 | GENERATING EVENT DEFINITIONS BASED ON SPATIAL AND RELATIONAL RELATIONSHIPS - Data from one or more sensors is input to a workflow and fragmented to produce HyperFragments. The HyperFragments of input data are processed by a plurality of Distributed Experts, who make decisions about what is included in the HyperFragments or add details relating to elements included therein, producing tagged HyperFragments, which are maintained as tuples in a Semantic Database. Algorithms are applied to process the HyperFragments to create an event definition corresponding to a specific activity. Based on related activity included in historical data and on ground truth data, the event definition is refined to produce a more accurate event definition. The resulting refined event definition can then be used with the current input data to more accurately detect when the specific activity is being carried out. | 09-18-2014 |
20140289173 | AUTOMATICALLY GENERATING AN ONTOLOGY AND AXIOMS FROM A BUSINESS-PROCESS MODEL - A method and associated systems for automatically generating an ontology and a set of axioms from a business-process model that represents the operations of a business. This ontology and set of axioms may be used to create the knowledgebase of an artificially intelligent expert system that emulates the business operations. A processor parses a representation of business processes stored in the business-process model, deriving a set of axioms and a set of entity classes from the parsed data. The processor uses these axioms and classes to identify concept nodes and process nodes, which it organizes into the ontology of the knowledgebase. The processor further identifies information derived from the parsed data to create a set of triple data items, each of which represents the information represented by one or more of the derived axioms. These triples are stored in the knowledgebase as a triple store data structure. | 09-25-2014 |
20140289174 | Data Analysis Computer System and Method For Causal Discovery with Experimentation Optimization - Discovery of causal models via experimentation is essential in numerous applications fields. One of the primary objectives of the invention is to minimize the use of costly experimental resources while achieving high discovery accuracy. The invention provides new methods and processes to enable accurate discovery of local causal pathways by integrating high-throughput observational data with efficient experimentation strategies. At the core of these methods are computational causal discovery techniques that account for multiplicity (i.e., indistinguishability) of causal pathways consistent with observational data. The invention, when applied for discovery of local causal pathways from a combination of observational and experimental data, achieves higher discovery accuracy than existing observational approaches and uses fewer experimental resources than existing experimental approaches. Repeated application of the invention for each variable in the modeled system produces the full causal model. | 09-25-2014 |
20140289175 | System and Method for Determining an Expert of a Subject on a Web-based Platform - Disclosed is a system for determining an expert of one or more subjects on a web-based platform. The system comprises a mining module for mining activity data of at least one user of a plurality of users from the web-based platform. The mining module may further compare the activity data with one or more subjects. The mining module may further label the activity data to a subject of the one or more subjects. A scoring module may assign performance points to the at least one user associated to the activity data. The scoring module may further assign subject points to the subject. The scoring module may further generate an activity gauge for the at least one user based on the performance points assigned and the subject points. The scoring module may further classify the at least one user as the expert of the subject. | 09-25-2014 |
20140289176 | Method and Apparatus for Extracting Entity Names and Their Relations - According to one embodiment of the invention, a method includes generating a person-name Information Gain (IG)-Tree and a relation IG-Tree from annotated data. The method also includes tagging and partial parsing of an input document. The names of the persons are extracted within the input document using the person-name IG-tree. Additionally, names of organizations are extracted within the input document. The method also includes extracting entity names that are not names of persons and organizations within the input document. Further, the relations between the identified entity names are extracted using the relation-IG-tree. | 09-25-2014 |
20140289177 | FINDING AND DISAMBIGUATING REFERENCES TO ENTITIES ON WEB PAGES - A system and method for disambiguating references to entities in a document. In one embodiment, an iterative process is used to disambiguate references to entities in documents. An initial model is used to identify documents referring to an entity based on features contained in those documents. The occurrence of various features in these documents is measured. From the number occurrences of features in these documents, a second model is constructed. The second model is used to identify documents referring to the entity based on features contained in the documents. The process can be repeated, iteratively identifying documents referring to the entity and improving subsequent models based on those identifications. Additional features of the entity can be extracted from documents identified as referring to the entity. | 09-25-2014 |
20140297570 | System And Method For High Accuracy Product Classification With Limited Supervision - Systems and methods are disclosed herein for classifying records, such as product records, using a machine learning algorithm. After training a classification model according to a machine learning algorithm using an initial training set, records are classified and high confidence classifications identified. Remaining classifications are submitted to a crowdsourcing forum that validates or invalidates the classifications or marks them as to unclear to evaluate. Invalidated classifications are automatically analyzed to identify one or both of classification values and categories having a high proportion of invalidated classifications. Requests are transmitted to analysts to generate training data that is added to the training set. The process of classifying records and obtaining crowdsourced validation thereof may then repeat. | 10-02-2014 |
20140297571 | Justifying Passage Machine Learning for Question and Answer Systems - Mechanisms are provided for generating an answer to an input question. An input question is received and a set of candidate answers is generated along with, for each candidate answer in the set of candidate answers, a corresponding selection of one or more selected evidence portions from a corpus of information providing evidence in support of the candidate answer being a correct answer for the input question. The candidate answers are ranked based on an application of a justifying passage model (JPM) to the selected evidence portions for each of the candidate answers in the set of candidate answers. The JPM identifies whether a candidate answer is justified by a selected evidence passage corresponding to the candidate answer. A candidate answer is output as the correct answer for the input question based on the ranking of the candidate answers. | 10-02-2014 |
20140297572 | METHOD AND SYSTEM FOR CLASSIFYING A PROTOCOL MESSAGE IN A DATA COMMUNICATION NETWORK - An intrusion detection method for detecting an intrusion in data traffic on a data communication network parses the data traffic to extract at least one protocol field of a protocol message of the data traffic, and associates the extracted protocol field with a model for that protocol field. The model is selected from a set of models. An assessment is made to determine if a contents of the extracted protocol field is in a safe region as defined by the model, and an intrusion detection signal is generated in case it is established that the contents of the extracted protocol field is outside the safe region. The set of models may comprise a corresponding model for each protocol field of a set of protocol fields. | 10-02-2014 |
20140304197 | INCREMENTAL MACHINE LEARNING FOR DATA LOSS PREVENTION - A computing device receives a document that was incorrectly classified as sensitive data based on a machine learning-based detection (MLD) profile. The computing device modifies a training data set that was used to generate the MLD profile by adding the document to the training data set as a negative example of sensitive data to generate a modified training data set. The computing device then analyzes the modified training data set using machine learning to generate an updated MLD profile. | 10-09-2014 |
20140304198 | Question-Related Identification of Relevant Social Communities - Methods, products, apparatus, and systems may identify one or more relevant social communities for one or more questions. Additionally, a user-question affinity value between a user and a question may be determined. In addition, a user-community affinity value between the user and each of a plurality of candidate social communities may be determined. Moreover, a question-community affinity value between the question and each of the plurality of candidate social communities may be determined based on the user-question affinity value and the user-community affinity value. The question-community affinity value determination may involve calculating a running average using the user-question affinity value and the user-community affinity value. The question-community affinity value may identify the one or more relevant social communities from the plurality of candidate social communities. | 10-09-2014 |
20140304199 | ESTIMATING ASSET SENSITIVITY USING INFORMATION ASSOCIATED WITH USERS - Automatically estimating a sensitivity level of an information technology (IT) asset in one aspect may obtain information about an asset. Characteristics of the asset assigned based on the information may be compared with stored characteristics of known sensitive assets. A sensitivity level of the asset may be determined based on the comparing. | 10-09-2014 |
20140304200 | ENHANCING DIAGNOSIS OF DISORDER THROUGH ARTIFICIAL INTELLIGENCE AND MOBILE HEALTH TECHNOLOGIES WITHOUT COMPROMISING ACCURACY - A computer system for generating a diagnostic tool by applying artificial intelligence to an instrument for diagnosis of a disorder, such as autism. For autism, the instrument can be a caregiver-directed set of questions designed for an autism classification tool or an observation of the subject in a video, video conference, or in person and associated set of questions about behavior that are designed for use in a separate autism classification tool. The computer system can have one or more processors and memory to store one or more computer programs having instructions for generating a highly statistically accurate set of diagnostic items selected from the instrument, which are tested against a first test using a technique using artificial intelligence and a second test against an independent source. Also, a computer implemented method and a non-transitory computer-readable storage medium are disclosed. | 10-09-2014 |
20140304201 | System And Method For Identifying Suggestions To Remedy Wind Turbine Faults - A method of automatically identifying suggestions regarding how to remedy wind turbine faults is provided, including establishing and storing a plurality of fault reports, each fault report including fault symptom information including data of identified fault symptoms of a previously occurred fault and fault remedy information, the fault remedy information including data regarding how the previously occurred fault was remedied, collecting fault related data, which includes data from one or more wind turbines exposed to an occurring fault, by a data processing arrangement performing data processing, including correlation of the fault related data with data from the stored fault reports, and based on the correlation establishing one or more fault remedy suggestions including suggestions regarding how to remedy the occurring fault related to the wind turbines based on the data processing. The invention also relates to a system for automatically identifying suggestions regarding how to remedy wind turbine faults. | 10-09-2014 |
20140310208 | Facilitating Operation of a Machine Learning Environment - Machine learning systems are represented as directed acyclic graphs, where the nodes represent functional modules in the system and edges represent input/output relations between the functional modules. A machine learning environment can then be created to facilitate the training and operation of these machine learning systems. | 10-16-2014 |
20140310209 | APPARATUS AND METHOD FOR SHARING TOPIC BETWEEN AUTONOMIC COMPUTING DEVICES - The present invention relates to an apparatus and method for sharing a topic between autonomic computing devices. The apparatus includes a knowledge base unit for storing information about abnormal states of an autonomic computing device. An autonomic computing management unit recognizes an abnormal state of the autonomic computing device based on the information about the abnormal states stored in the knowledge base unit, learns self-management for solving the abnormal state, and, when information about a new abnormal state is acquired during learning of self-management, generates a topic from the information about the new abnormal state. A topic transmission/reception unit transmits the topic generated by the autonomic computing management unit to a plurality of autonomic computing devices present in an identical domain or receives topics transmitted from the plurality of autonomic computing devices. | 10-16-2014 |
20140310210 | METHOD AND DEVICE FOR CREATING A FUCNTION MODEL FOR A CONTROL UNIT OF AN ENGINE SYSTEM - A computerized method for creating a function model based on a non-parametric, data-based model, e.g., a Gaussian process model, includes: providing training data including measuring points having one or multiple input variables, the measuring points each being assigned an output value of an output variable; providing a basic function; modifying the training data with the aid of difference formation between the function values of the basic function and the output values at the measuring points of the training data; creating the data-based model based on the modified training data; and providing the function model as a function of the data-based model and the basic function. | 10-16-2014 |
20140310211 | Method and device for creating a nonparametric, data-based function model - A method for creating a nonparametric, data-based function model having measuring points in multiple training data records, including the following: providing weighting specifications for the measuring points of each training data record; forming a set union of the measuring points of the multiple training data records; and creating the nonparametric function model from the set union of the measuring points of the training data records according to an algorithm which is dependent on the weighting specifications for the measuring points of the multiple training data records. | 10-16-2014 |
20140310212 | METHOD AND DEVICE FOR CREATING A NONPARAMETRIC, DATA-BASED FUNCTION MODEL - A method for ascertaining a nonparametric, data-based function model, in particular a Gaussian process model, using provided training data, the training data including a number of measuring points which are defined by one or multiple input variables and which each have assigned output values of at least one output variable, including: selecting one or multiple of the measuring points as certain measuring points or adding one or multiple additional measuring points to the training data as certain measuring points; assigning a measuring uncertainty value of essentially zero to the certain measuring points; and ascertaining the nonparametric, data-based function model according to an algorithm which is dependent on the certain measuring points of the modified training data and the measuring uncertainty values assigned in each case. | 10-16-2014 |
20140310213 | USER-CENTRIC SOFT KEYBOARD PREDICTIVE TECHNOLOGIES - An apparatus and method are disclosed for providing feedback and guidance to touch screen device users to improve text entry user experience and performance by generating input history data including character probabilities, word probabilities, and touch models. According to one embodiment, a method comprises receiving first input data, automatically learning user tendencies based on the first input data to generate input history data, receiving second input data, and generating auto-corrections or suggestion candidates for one or more words of the second input data based on the input history data. The user can then select one of the suggestion candidates to replace a selected word with the selected suggestion candidate. | 10-16-2014 |
20140317029 | SELECTIVE CARRYING OUT OF SCHEDULED CONTROL OPERATIONS BY AN INTELLIGENT CONTROLLER - The current application is directed to intelligent controllers that use sensor output and electronically stored information to determine whether or not one or more types of entities are present within an area, volume, or environment monitored by the intelligent controllers. The intelligent controllers select operational modes and/or modify control schedules with respect to the presence and absence of the one or more entities. The intelligent controllers selectively carry out scheduled control operations during periods of time when one or more types of entities are determined not to be in a controlled environment. | 10-23-2014 |
20140317030 | METHOD AND APPARATUS FOR CUSTOMIZING CONVERSATION AGENTS BASED ON USER CHARACTERISTICS - A conversation-simulating system facilitates simulating an intelligent conversation with a human user. During operation, the system can receive a user-statement from the user during a simulated conversation, and generates a set of automatic-statements that each responds to the user-statement. The system then determines a set of behavior-characteristics for the user, and computes relevance scores for the automatic-statements based on the behavior-characteristics. Each relevance score indicates an outcome quality that the user is likely to perceive for the automatic-statement as a response to the user-statement. The system selects an automatic-statement that has a highest relevance score from the set of automatic-statements, and provides the selected automatic-statement to the user. | 10-23-2014 |
20140317031 | APPLICATION RECOMMENDATION - Various embodiments of the disclosed technology can obtain information about associations between users (e.g., user accounts) of a content management system and applications compatible with the content management system. Various embodiments can also obtain information about a plurality of attributes associated with usage of the content management system by the users (e.g., user accounts). In some embodiments, the attributes can include a device property, a usage pattern, an account property, a content item property, a profile property, a preference property, or a domain property. Moreover, data about social connections of the users (e.g., user accounts) can also be obtained. Based, at least in part, on at least one of the information about the associations, the information about the plurality of attributes, or the data about the social connections, one or more applications can be recommended to a selected user (e.g., a selected user account). | 10-23-2014 |
20140317032 | Systems and Methods for Generating Automated Evaluation Models - Systems and methods are described for generating a scoring model for responses. A computer-implemented method of calibrating a scoring model using a processing system for scoring examinee responses includes accessing a plurality of training responses for training the scoring model. The plurality of training responses are analyzed to derive values of multiple features (variables) of the training responses. The scoring model is trained based on the values of the multiple features of the training responses and one or more external measures of proficiency for each individual associated with a training response utilized in the training. The one or more external measures are not derived from the training responses. Based on the training, a weight for each of the multiple features is determined. The scoring model is calibrated to include the weights for at least some of the features for scoring examinee responses. | 10-23-2014 |
20140324739 | SYSTEMS AND METHODS FOR LEARNING OF NORMAL SENSOR SIGNATURES, CONDITION MONITORING AND DIAGNOSIS - Systems and methods to monitor a signal from an apparatus are disclosed. A feature extracted from the signal is automatically defined. Signals are received over a period of time wherein the apparatus is in a normal operational mode. Features are classified in a learning mode and are applied to create a reference model that defines a within-normal operational mode. In a testing mode a signal generated by the apparatus is received, a feature is extracted and classified. Instantaneous data generated in operational mode by the apparatus is classified by the system as abnormal if it does not lie within boundaries of the reference model or contains information/structure in an orthogonal subspace. A learned reference model is augmented by a user or automatically. In one illustrative example the apparatus is a power generation equipment and the signal is an acoustic signal. | 10-30-2014 |
20140324740 | Ontology-Based Attribute Extraction From Product Descriptions - Systems and methods are disclosed herein for obtaining a structured listing of attributes and corresponding values based on an unstructured document, such as a product description in a product record. Putative values are identified in the document and corresponding candidate attributes are identified in a taxonomy. Attribute-value pairs are then evaluated with respect to a plurality of rules. Attribute-value pairs and outputs of the one or more rules are evaluated using a machine-learning algorithm, such as a decision tree, in order to determine which attribute-value pairs to retain. Retained attribute-value pairs are stored and used to respond to search queries and facilitate comparison of products. Attributes selected may also be used to update a product template. | 10-30-2014 |
20140324741 | METHODS AND SYSTEMS OF CLASSIFYING SPAM URL - A method of operation of a URL spam detection system includes: identifying a feature dimension of a user action on a social networking system to detect anomalies; extracting URL chunks from a content associated with the user action; aggregating a non-content feature of the user action along the feature dimension into a URL distribution store to produce a feature distribution for each of the URL chunks; determining whether the feature distribution of a particular URL chunk within the URL chunks exceeds an expectation threshold for the feature dimension; and classifying the particular URL chunk as an illegitimate URL when the feature distribution exceeds the expectation threshold to restrict access to a particular URL chunk on a social networking system. | 10-30-2014 |
20140324742 | SUPPORT VECTOR MACHINE - A method of building a classification model using a SVM training module comprising, with a processor, computing a mean value of a number of training vectors received by the processor, subtracting the mean value of the number of training vectors from each training vector received by the processor to obtain a number of difference vectors, applying a hash function to each of the difference vectors to obtain a number of hashed vectors, and applying a linear training formula to the hashed vectors to obtain a classifier model. Classifying a sample vector comprises, with a processor, subtracting a mean value of a number of support vector machine training vectors from the sample vector to obtain a sample difference vector, with a processor, applying a hash function to the sample difference vector to obtain a hashed sample vector, and classifying the hashed sample vector using a classifier model. | 10-30-2014 |
20140324743 | AUTOREGRESSIVE MODEL FOR TIME-SERIES DATA - A technique includes fitting an autoregressive integrated moving average (ARIMA) model to time-series data. The technique further includes the computation of autoregression coefficients from the ARIMA model applied to the time-series data. The autoregression coefficients may be usable for data classification purposes. | 10-30-2014 |
20140324744 | Decision Tree With Compensation For Previously Unseen Data - A computer-implemented method is disclosed for efficiently processing records with unseen data. In the method, a computer system may obtain a plurality of records and a decision tree generated in a learning process. The decision tree may include a distinction node having multiple paths extending therefrom. After arriving at the distinction node with one or more records, the computer system may determine that the one or more records correspond to data of a type not seen by the distinction node in the learning process. Thereafter, the computer system may depart the distinction node via each of the multiple paths and eventually reach multiple leaf nodes of the decision tree. Each of the multiple leaf nodes may correspond to a probability distribution. Accordingly, the computer system may combine the probability distribution of each of the multiple leaf nodes to obtain a hybrid probability distribution corresponding to the one or more records. | 10-30-2014 |
20140324745 | METHOD, AN APPARATUS AND A COMPUTER SOFTWARE FOR CONTEXT RECOGNITION - Various embodiments relate to a context recognition. Classification of a context is performed by using features received from at least one sensor of a client device, and model parameters being defined by a training data to output a result and a likelihood of the context. The result is shown to the user, who provides feedback regarding the result. The features, result, likelihood, and the feedback are stored, whereby the model parameters are adapted using the features, result, likelihood and the feedback to obtain adapted model parameters. The result, likelihood and the feedback can also be used for performing confidence estimation to obtain a confidence value. The confidence value can then be used for performing an action, e.g. adding a new sensor, adding a new feature, changing a device profile, launching an application. | 10-30-2014 |
20140330755 | INTELLIGENT SEARCHING OF ELECTRONICALLY STORED INFORMATION - Technologies and implementations for training a predictive intelligence associated with electronic discovery (e-discovery) are generally disclosed. | 11-06-2014 |
20140330756 | AUTOMATED ALERTING RULES RECOMMENDATION AND SELECTION - An improved technique involves a device monitoring system providing alerting rules for a particular computing environment automatically based on existing alerting rules sets for other computing environments. Along these lines, when an IT professional monitors a computing environment through the device monitoring system, the device monitoring system stores alerting rules sets for that computing environment in a database. In storing rules sets and other information about that and other computing environments, the device monitoring system acquires intelligence from a wealth of data concerning how other IT professionals react to configuration changes in their computing environments. In this way, the device monitoring system then suggests alerting rules for a particular computing environment whose alerting rules are found to be suboptimal based on performance data from the particular computing environment. | 11-06-2014 |
20140330757 | SELECTING STRANGERS FOR INFORMATION SPREADING ON A SOCIAL NETWORK - A computer-implemented method, computer program product, and computer system for selecting strangers for information spreading on a social network. Statistical models are trained with history data of the information spreading of strangers on the social network. The strangers on the social network are users of the social network and not related to each other. For the strangers on the social network, information spreading probabilities based on features, information reach, and information spreading probabilities based on a wait time. Fitness scores of the strangers on the social network are computed; the fitness scores are a function of information spreading probabilities based on features, information reach, and information spreading probabilities based on the wait time. The strangers on the social network are ranked, based on the fitness scores, in a sorted set. One or more of the strangers for the information spreading are selected from the sorted set. | 11-06-2014 |
20140330758 | FORMAL VERIFICATION RESULT PREDICTION - A design verification problem includes a design description and a property to be verified. Feature data is identified from the design verification problem and a result is predicted for the design verification problem based on the feature data. A plurality of verification engines is then orchestrated based on the prediction. Supervised machine learning may be used for the result prediction. Feature data and verification results from a plurality of training test cases are used to train a classifier to create a prediction model. The prediction model uses the feature data of the design verification problem to make a result prediction for the design verification model. | 11-06-2014 |
20140330759 | SYSTEM AND METHOD FOR DEVELOPING A RISK PROFILE FOR AN INTERNET SERVICE - A method and system for controlling access to an Internet resource is disclosed herein. When a request for an Internet resource, such as a Web site, is transmitted by an end-user of a LAN, a security appliance for the LAN analyzes a reputation index for the Internet resource before transmitting the request over the Internet. The reputation index is based on a reputation vector which includes a plurality of factors for the Internet resource such as country of domain registration, country of service hosting, country of an internet protocol address block, age of a domain registration, popularity rank, internet protocol address, number of hosts, to-level domain, a plurality of run-time behaviors, JavaScript block count, picture count, immediate redirect and response latency. If the reputation index for the Internet resource is at or above a threshold value established for the LAN, then access to the Internet resource is permitted. If the reputation index for the Internet resource is below a threshold value established for the LAN, then access to the Internet resource is denied. | 11-06-2014 |
20140330760 | CONTENT DISTRIBUTION - The distribution of content items, such as news items, in a news publishing platform is governed by a plurality of interrelated factors. These factors include publisher bias, trust bias, and user-specific bias, which reflect the user's reading history, the social clusters to which the user belongs, the user's location etc. To model the relevance of each content item to a given user, a metric of the overall story strength is calculated in accordance with one or more of these biases. The content item is delivered to the target in accordance with the story strength metric. | 11-06-2014 |
20140337254 | RECOMMENDING ACTIONS FOR SOCIAL MEDIA ENGAGEMENTS - A recommendation of a direct engagements is made. A topic is received from a recommendee system, and a social media post is received from a social media server. A first and second information about the source of the social media post are determined. The first information can include the location of the source of the social media post, the readiness for engagement of the source of the social media post, or the personality of the source of the social media post. The second information can include the relevance of the content of the social media post to the topic, the opinion of the content of the social media post about the topic, or the intent of the content of the social media post regarding the topic. A direct engagement to be performed on the source based on the first information and the second information is determined. | 11-13-2014 |
20140337255 | SCALABLE, MEMORY-EFFICIENT MACHINE LEARNING AND PREDICTION FOR ENSEMBLES OF DECISION TREES FOR HOMOGENEOUS AND HETEROGENEOUS DATASETS - Optimization of machine intelligence utilizes a systemic process through a plurality of computer architecture manipulation techniques that take unique advantage of efficiencies therein to minimize clock cycles and memory usage. The present invention is an application of machine intelligence which overcomes speed and memory issues in learning ensembles of decision trees in a single-machine environment. Such an application of machine intelligence includes inlining relevant statements by integrating function code into a caller's code, ensuring a contiguous buffering arrangement for necessary information to be compiled, and defining and enforcing type constraints on programming interfaces that access and manipulate machine learning data sets. | 11-13-2014 |
20140337256 | INFLUENCE LEARNING IN AN ENVIRONMENTALLY MANAGED SYSTEM - Systems and methods are described for updating an influence model used to manage physical conditions of an environmentally controlled space. A method comprises operating an environmental maintenance system in a first production mode with the influence model until an event causes the system to enter a second production mode. In the second production mode a first actuator's operation level is varied and operation levels of other actuators are optimized. The influence model is adjusted based on the operation levels. | 11-13-2014 |
20140337257 | HYBRID HUMAN MACHINE LEARNING SYSTEM AND METHOD - Embodiments of the present invention provide a system, method, and article of hybrid human machine learning system with tagging and scoring techniques for sentiment magnitude scoring of textual passages. The combination of machine learning systems with data from human pooled language extraction techniques enable the present system to achieve high accuracy of human sentiment measurement and textual categorization of raw text, blog posts, and social media streams. This information can then be aggregated to provide brand and product strength analysis. A data processing module is configured to get streaming data and then tag the streaming data automatically using the machine learning output. A crowdsourcing module is configured to select a subset of social media posts that have been previously stored in the database, and present the social media posts on the web, which then tags each social media with a selected set of attributes. A score aggregator module configured to provide a score based on a user's feedback for each social media post. | 11-13-2014 |
20140337258 | METHODS FOR MAPPING DATA INTO LOWER DIMENSIONS - Methods and systems for creating ensembles of hypersurfaces in high-dimensional feature spaces, and to machines and systems relating thereto. More specifically, exemplary aspects of the invention relate to methods and systems for generating supervised hypersurfaces based on user domain expertise, machine learning techniques, or other supervised learning techniques. These supervised hypersurfaces may optionally be combined with unsupervised hypersurfaces derived from unsupervised learning techniques. Lower-dimensional subspaces may be determined by the methods and systems for creating ensembles of hypersurfaces in high-dimensional feature spaces. Data may then be projected onto the lower-dimensional subspaces for use, e.g., in further data discovery, visualization for display, or database access. Also provided are tools, systems, devices, and software implementing the methods, and computers embodying the methods and/or running the software, where the methods, software, and computers utilize various aspects of the present invention relating to analyzing data. | 11-13-2014 |
20140344193 | TUNING HYPER-PARAMETERS OF A COMPUTER-EXECUTABLE LEARNING ALGORITHM - Technologies pertaining to tuning a hyper-parameter configuration of a learning algorithm are described. The learning algorithm learns parameters of a predictive model based upon the hyper-parameter configuration. Candidate hyper-parameter configurations are identified, and statistical hypothesis tests are undertaken over respective pairs of candidate hyper-parameter configurations to identify, for each pair of candidate hyper-parameter configurations, which of the two configurations is associated with better predictive performance. The technologies described herein take into consideration the stochastic nature of training data, validation data, and evaluation functions. | 11-20-2014 |
20140344194 | MACHINE-LEARNING ACCELERATOR (MLA) INTEGRATED CIRCUIT FOR EXTRACTING FEATURES FROM SIGNALS AND PERFORMING INFERENCE COMPUTATIONS - A machine-learning accelerator (MLA) integrated circuit for extracting features from signals and performing inference computations is disclosed. The MLA integrated circuit includes a framework of finite state machine (FSM) kernels that are machine-learning algorithms implemented in hardware. The MLA integrated circuit further includes a kernel controller having mathematical structures implemented in hardware in communication with the framework of FSM kernels. An arithmetic engine implemented in hardware within the MLA integrated circuit is in communication with the kernel controller to perform computations for the mathematical structures. In at least one embodiment, the MLA integrated circuit includes a compression decompression accelerator (CDA) implemented in hardware and coupled between a memory and the kernel controller for compressing data to be stored in the memory and for decompressing data retrieved from the memory. | 11-20-2014 |
20140344195 | SYSTEM AND METHOD FOR MACHINE LEARNING AND CLASSIFYING DATA - The present invention relates in general to the field of parallel data processing, and more particularly to machine learning and classification of extremely large volumes of unstructured gene sequence data using Collaborative Analytics Gene Sequence Classification Learning Systems and Methods. | 11-20-2014 |
20140351176 | ACTIVE LEARNING ON STATISTICAL SERVER NAME EXTRACTION FROM INFORMATION TECHNOLOGY (IT) SERVICE TICKETS - Access is obtained to a plurality of information technology services problem tickets. At least a first subset of the tickets include free text tickets with server names embedded in unstructured text fields. The server names are extracted from the first subset of the tickets via a statistical machine learning technique. Using the extracted server names, those of the first subset of the tickets from which the server names have been extracted are linked to corresponding server entries in a configuration information database to facilitate resolution of problems associated with the first subset of the tickets from which the server names have been extracted; and/or at least one of the extracted server names is identified as missing from a list of known server names. | 11-27-2014 |
20140351177 | SYSTEM FOR PREDICTING THICKNESS OF BATTERY AND METHOD FOR PREDICTING THICKNESS OF BATTERY - A system for predicting the thickness of a battery is disclosed. In one aspect, the battery thickness predicting system includes a learning data input unit for receiving data on a previously manufactured battery. The thickness predicting system further includes an object data input unit for receiving data on a battery whose thickness is to be predicted. The system further comprises a mechanical learning unit connected to the learning data input unit to obtain a predicting function based on learning factors input to the learning data input unit and to provide weight values to the learning factors, respectively. The system further includes a thickness predicting unit connected to the object data input unit and the mechanical learning unit and using the weight values provided by the mechanical learning unit in order to predict the thickness of the battery whose thickness is to be predicted. | 11-27-2014 |
20140351178 | ITERATIVE WORD LIST EXPANSION - Methods and systems are provided for expanding an electronic word list, containing a set of words where each word is associated with a label from a first set of labels. A subset of training data containing a set of texts having a second set of labels is obtained. For each word in the electronic word list and a label in the sub-set of the training data, a feature selection criterion is calculated. One or more words are selected, for which resulting value of the feature selection criterion calculation is greater than a predetermined threshold value. The one or more selected words are added to the electronic word list. | 11-27-2014 |
20140351179 | INFORMATION PUSH METHOD AND APPARATUS - The present invention is applicable to the field of information processing technologies, and provides an information push method and apparatus. The method includes: acquiring historical behavior information of a user from a social data source; dividing, according to a preset rule, the acquired historical behavior information into one or more documents related to user behavior information; obtaining a model according to the document and by using a statistical learning method; and generating push information based on the model, and sending the push information to a client where a corresponding user is located. The push information in the present invention is generated based on the historical behavior information of the user, so that accuracy of information push can be effectively improved. | 11-27-2014 |
20140351180 | Learning Device With Continuous Configuration Capability - An embodiment method for continuous configuration of learning devices includes operations for storing, by a learning device within a decentralized system of a plurality of learning devices, events obtained while in a monitoring mode, activating a triggered mode for a reflex when at least one of the stored events corresponds to a trigger pattern, determining whether the reflex has a trigger weight exceeding a trigger weight threshold, conducting the predetermined action associated with the reflex when the trigger weight exceeds the trigger weight threshold, obtaining at least one additional event while in the triggered mode, adjusting the trigger weight of the reflex when the at least one additional event corresponds to a correction pattern or a reward pattern occurring in response to conducting the predetermined action, and creating a second reflex when the at least one additional event does not correspond to a known pattern. | 11-27-2014 |
20140351181 | REQUESTING PROXIMATE RESOURCES BY LEARNING DEVICES - Various embodiments for a learning device to improve the performance of learned behaviors by requesting information from proximate devices within a decentralized system including a learning device method for generating, by the learning device, a first pattern based upon one or more obtained events, determining whether the first pattern exactly matches a known second pattern, determining whether the first pattern matches the second pattern within a predefined threshold in response to determining that the first pattern does not exactly match the second pattern, identifying a missing event of the second pattern in response to determining that the first pattern matches the second pattern within the predefined threshold, and broadcasting, by the learning device, a message requesting data related to the identified missing event. Data received in response to request messages may be used to recognize that the known second pattern is matched. | 11-27-2014 |
20140351182 | Modifying Learning Capabilities of Learning Devices - Various embodiments for modifying learning capabilities within a decentralized system of learning devices, a method including receiving, at a learning device, a signal from a nearby device, determining whether the received signal is a learning modifier signal based on data within the received signal, and modifying one or more of the learning capabilities in response to determining that the received signal is the learning modifier signal. The method may further include determining whether subsequent learning modifier signals are received, and resetting the modified one or more of the learning capabilities in response to determining that the subsequent learning modifier signals are not received. Modifying learning capabilities may include enabling or disabling a learning mode of the learning device and/or adjusting values of variables used to calculate trigger weights of reflexes. When subsequent learning modifier signals are not received, the learning device may reset modified learning capabilities. | 11-27-2014 |
20140351183 | METHODS AND RELATED SYSTEMS OF BUILDING MODELS AND PREDICTING OPERATIONAL OUTCOMES OF A DRILLING OPERATION - Building models and predicting operational outcomes of a drilling operation. At least some of the illustrative embodiments are methods including: gathering sensor data regarding offset wells and context data regarding the offset wells, and placing the sensor data and context data into a data store; creating a reduced data set by identifying a correlation between data in the data store and an operational outcome in a drilling operation; creating a model based on the reduced data set; and predicting the operational outcome based on the model. | 11-27-2014 |
20140351184 | USER SPECIFIC PLAN GENERATION METHOD AND SYSTEM - The present invention envisages a personalized plan generation system and a method that satisfies maximum user preferences and constraints; besides including a number of enabling features like of plan repair or revision with dynamically changing situations or contextual information. Moreover, the system is able to perform a collaborative planning by opinion mining in social networks to achieve better optimization. Significantly, the explanation for the selection of plan steps or a change in plan altogether can be expressed in natural language. | 11-27-2014 |
20140351185 | MACHINE LEARNING MEMORY MANAGEMENT AND DISTRIBUTED RULE EVALUATION - Aspects of the present disclosure relate to management of evaluated rule data sets. Specifically, a unreduced evaluated rule data set may contain a number of items to be compared or analyzed according to a number of rules, and may also contain the results of such analysis. An illustrative reduced evaluated data set can include the results of evaluated rules. When utilized in conjunction with an item data set and a rule data set, the information contained within the unreduced evaluated rule data set may be maintained. The reduce memory requirements of the reduced evaluated rule data set may facilitate storage of the reduced evaluated rule data set in faster to access memory, or may facilitate distributed computation of the reduced evaluated rule data set. | 11-27-2014 |
20140358828 | MACHINE LEARNING GENERATED ACTION PLAN - Apparatuses, systems, methods, and computer program products are disclosed for a machine learning generated action plan. A machine learning module is configured to process different instances of data using machine learning to produce one or more results. The different instances of data may comprise different values for one or more actionable features. A recommended action module is configured to select one or more recommended actions for achieving a goal associated with the machine learning. The recommended action module may select the one or more recommended actions based on the one or more results. An action plan interface module is configured to provide an action plan associated with the one or more recommended actions. | 12-04-2014 |
20140358829 | System and method for sharing record linkage information - Herein disclosed is a system and method for record linkage that uses machine learning to link records, so that many users can contribute their training data to a shared repository and employ the accumulated training data without any user having to share their actual data. The system includes a record linkage server, which further includes a record linkage repository, a domain classifier, and a domain classification trainer. The record linkage server is connected with a record linkage client, which includes a field comparator and a manual label prompter. Further disclosed is a method for record linkage, describing how two structured data sets can be matched, including searching domains, loading data sets, loading domain, matching fields, iterating record linking for all record pairs, including: selecting record pair, calculating comparison vector, calculating label probabilities, determining label, optionally setting label manually, updating prior probabilities, optionally confirming selected label, and updating training data. | 12-04-2014 |
20140358830 | LITHOGRAPHIC HOTSPOT DETECTION USING MULTIPLE MACHINE LEARNING KERNELS - A hotspot detection system that classifies a set of hotspot training data into a plurality of hotspot clusters according to their topologies, where the hotspot clusters are associated with different hotspot topologies, and classifies a set of non-hotspot training data into a plurality of non-hotspot clusters according to their topologies, where the non-hotspot clusters are associated with different topologies. The system extracts topological and non-topological critical features from the hotspot clusters and centroids of the non-hotspot clusters. The system also creates a plurality of kernels configured to identify hotspots, where each kernel is constructed using the extracted critical features of the non-hotspot clusters and the extracted critical features from one of the hotspot clusters, and each kernel is configured to identify hotspot topologies different from hotspot topologies that the other kernels are configured to identify. | 12-04-2014 |
20140358831 | SYSTEMS AND METHODS FOR BAYESIAN OPTIMIZATION USING NON-LINEAR MAPPING OF INPUT - Techniques for use in connection with performing optimization using an objective function that maps elements in a first domain to values in a range. The techniques include using at least one computer hardware processor to perform: identifying a first point at which to evaluate the objective function at least in part by using an acquisition utility function and a probabilistic model of the objective function, wherein the probabilistic model depends on a non-linear one-to-one mapping of elements in the first domain to elements in a second domain; evaluating the objective function at the identified first point to obtain a corresponding first value of the objective function; and updating the probabilistic model of the objective function using the first value to obtain an updated probabilistic model of the objective function. | 12-04-2014 |
20140358832 | Personalized Activity Stream Discovery System, Method, and Device - A personalized activity stream system, method, and device delivers a recommendation that is contextualized basis a selected item in an activity stream and that is personalized for a recommendation recipient consistent with an inference of the recommendation recipient's interests. Contextualization and personalization may be based on an automatic analysis of usage behaviors and/or content. The recommendation may be informed by an inference of an expertise level, and an explanation for the recommendation may be delivered to the recommendation recipient. The recommendation may be in accordance with an automatically determined geographic location associated with a location-aware portable device, as well as environmental conditions proximal to the portable device. | 12-04-2014 |
20140365408 | SYSTEM AND METHOD FOR MANAGING BEHAVIOR CHANGE APPLICATIONS FOR MOBILE USERS - An example system and method for managing behavior change applications for mobile users is disclosed. In an embodiment of the system and method, data on a plurality of users in a behavioral change program is received from a plurality of devices operating in a communications network. The data is used to determine plurality of user segments. Each user of the plurality of users is classified into a one of the plurality of user segments. Then a plurality of behavioral models is determined, where each of the plurality of behavioral models comprises a statistical model of relations between behaviors of users within a corresponding one of the plurality of user segments. An intervention model is then determined for a user, where the intervention model is based on a behavior model of the user segment to which the user belongs. Interventions are then sent to a device of the user according to the intervention model. | 12-11-2014 |
20140365409 | Determining Well Parameters For Optimization of Well Performance - Systems and methods for determining well parameters for optimization of well performance. The method includes training, via a computing system, a well performance predictor based on field data corresponding to a hydrocarbon field in which a well is to be drilled. The method also includes generating, via the computing system, a number of candidate well parameter combinations for the well and predicting, via the computing system, a performance of the well for each candidate well parameter combination using the trained well performance predictor. The method further includes determining, via the computing system, an optimized well parameter combination for the well such that the predicted performance of the well is maximized. | 12-11-2014 |
20140365410 | APPARATUS AND METHOD FOR BUILDING AND USING INFERENCE ENGINES BASED ON REPRESENTATIONS OF DATA THAT PRESERVE RELATIONSHIPS BETWEEN OBJECTS - This disclosure describes, among other things, an apparatus for generating an inference engine about a document. The apparatus includes at least one processor and a memory with instructions. The memory including instructions that, when executed by the at least one processor, cause the at least one processor to perform a number of processes. The processor accesses a set of documents. Each document has a corresponding inference. The processor also generates a vector representation for each document in the set of documents. First, the processor parses text of the document into groups of words, and generates a vector representation for each group. | 12-11-2014 |
20140365411 | Monitoring Health of Dynamic System Using Speaker Recognition Techniques - Monitoring health of dynamic systems includes using speaker recognition techniques. Some embodiments include determining a system-independent statistical (first) model, and determining a healthy system (second) model based on data representing vibrations of multiple healthy systems and the first model and speaker recognition techniques. Vibration data are obtained from a particular system. It is determined whether the particular system is unhealthy based on the vibration data from the particular system and the first model and the second model and speaker recognition techniques. Some embodiments include obtaining training data that represents vibrations of multiple healthy systems. A damage-sensitive parameter is based on the training data. A threshold value that separates damaged systems from healthy systems is based on the training data and the parameter. It is determined whether a particular system is healthy based on the threshold value and a value for the parameter for vibration data from the particular system. | 12-11-2014 |
20140372346 | DATA INTELLIGENCE USING MACHINE LEARNING - Apparatuses, systems, methods, and computer program products are presented for performing data analytics using machine learning. An unsupervised learning module is configured to assemble an unstructured data set into multiple versions of an organized data set. A supervised learning module is configured to generate one or more machine learning ensembles based on each version of multiple versions of an organized data set and to determine which machine learning ensemble exhibits a highest predictive performance. | 12-18-2014 |
20140372347 | METHODS AND SYSTEMS FOR IDENTIFYING ACTION FOR RESPONDING TO ANOMALY IN CLOUD COMPUTING SYSTEM - Methods performed by a physical computing system include automatically identifying, using at least one trained classifier, an action for responding to an anomaly in the execution of the application in a cloud computing system. The at least one trained classifier relates a metrics set to a result of performing an action for addressing an anomaly. Systems and computer readable media are also described herein. | 12-18-2014 |
20140372348 | REAL-TIME ANOMALY DETECTION OF CROWD BEHAVIOR USING MULTI-SENSOR INFORMATION - The present disclosure includes systems and methods for detecting an anomaly in crowd behavior. The method includes receiving sensor data representing a crowd, and partitioning the sensor data into local areas forming neighborhoods. The method further includes, for each local area, characterizing motion in the local area to determine real-time estimates of motion of sub-populations based on the sensor data, providing a crowd model for each local area, representing continuous functions describing expected motion near each local area, and determining parametric values of the crowd model based on the real-time estimates of the motion of the sub-populations. The method further includes learning and adapting auxiliary stochastic models characterizing normal evolution of the parametric values of the crowd model over time associated with each local area, and identifying a potential anomaly associated with the local area by comparing predictions from an auxiliary stochastic model with parametric values of the crowd model. | 12-18-2014 |
20140372349 | METHOD OF MACHINE LEARNING CLASSES OF SEARCH QUERIES - A computer-implemented method of determining search intent, comprises: receiving a search query; searching content across a plurality of content classes using the search query, so as to obtain a plurality of search results; deriving summary data from the search results; applying the summary data to a trained machine learning model; and determining from the machine learning model a selected one of the content classes corresponding to the search intent of the search query. | 12-18-2014 |
20140372350 | System, A Method and a Computer Program Product for Performance Assessment - A system, a computerized method, and a computerized service center for classification of items based on their attributes and on a classification scheme that is defined based on information pertaining to each item of a set of items, and which is indicative of: (a) a quantity of occurrences of the item in a sample; (b) a quantity of successful occurrences of the item in the sample; and (c) at least one attribute of the item with regard to at least one variable out of a set of variables. | 12-18-2014 |
20140372351 | RULE-BASED ITEM CLASSIFICATION - Systems and methods are disclosed herein for rule-based item classification. The methods include receiving, by a computing device, an item record for analysis. The computing device may determine ranked lists of item types using rule-based classifiers and machine learning-based classifiers. Then, the computing device may aggregate the ranked lists of item types to generate a combined ranked list of item types. | 12-18-2014 |
20140379617 | METHOD AND SYSTEM FOR RECOMMENDING INFORMATION - Embodiments of the present application relate to a method for recommending information, a system for recommending information, and a computer program product for recommending information. A method for recommending information is provided. The method includes determining a set of specific first users comprising at least one specific first user who complies with a first preset condition, the determination being based on operating behavior information of a set of one or more first users recorded in a system, looking up, in the set of specific first users, targeted specific first users having a similarity to a current user who complies with a second preset condition, and providing recommendation information to the current user based on the operating behavior information of the targeted specific first users. | 12-25-2014 |
20140379618 | DOCUMENT RECOMMENDATION - Embodiments of the present disclosure provide a method and apparatus for document recommendation by obtaining a plurality of first data for a source recommendation task from activities related to a source user using a plurality of first documents; obtaining a plurality of second data for a target recommendation task from activities related to a target user using a plurality of second documents; performing the target recommendation task based on the plurality of first data, the plurality of second data, and knowledge transferred from the source recommendation task to obtain a target recommendation model; and conducting document recommendation to the target user using the target recommendation model. | 12-25-2014 |
20140379619 | Automated System For Generative Multimodel Multiclass Classification And Similarity Analysis Using Machine Learning - A sample of data is placed within a directed graph that comprises a plurality of hierarchical nodes that form a queue of work items for a particular worker class that are used to process the sample of data. Subsequently, work items are scheduled within the queue for each of a plurality of workers by traversing the nodes of the directed graph. The work items are then served to the workers according to the queue. Results can later be received from the workers for the work items (the nodes of the directed graph are traversed based on the received results). In addition, in some variations, the results can be classified so that one or models can be generated. Related systems, methods, and computer program products are also described. | 12-25-2014 |
20140379620 | SYSTEMS AND METHODS FOR AUTOMATIC SEGMENT SELECTION FOR MULTI-DIMENSIONAL BIOMEDICAL SIGNALS - Systems and methods for automatically analyzing and selecting prominent channels from multi-dimensional biomedical signals in order to detect particular diseases or ailments are provided. Such systems and methods may be applied in different ways to obtain numerous benefits, such as lowering of power and processing requirements, reducing an amount of data acquired, simplifying hardware deployment, detecting non-trivial patterns, obtaining, clinical episode prognosis, improving patient care, and/or the like. | 12-25-2014 |
20140379621 | SYSTEM, METHOD AND COMPUTER READABLE MEDIUM FOR DETERMINING AN EVENT GENERATOR TYPE - Human interaction with a webpage may be determined by processing an event stream generated by the client device during the webpage interaction. A classification server receives the event stream and compares components of the event stream, including components of an event header message, with prerecorded datasets. The datasets include prerecorded event streams having a known interaction type. Training clients may be provided for generating the prerecorded datasets. | 12-25-2014 |
20150012465 | DECISION TREE LEARNING - A method of generating a decision tree is provided. A leaf assignment for each proposed split in generating the decision tree is incremented using a Gray code. | 01-08-2015 |
20150012466 | METHOD FOR A BRAIN REGION LOCATION AND SHAPE PREDICTION - A volumetric segmentation method is disclosed for brain region analysis, in particular but not limited to, regions of the basal ganglia such as the subthalamic nucleus (STN). This serves for visualization and localization within the sub-cortical region of the basal ganglia, as an example of prediction of a region of interest for deep brain stimulation procedures. A statistical shape model is applied for variation modes of the STN, or the corresponding regions of interest, and its predictors on high-quality training sets obtained from high-field, e.g., 7T, MR imaging. The partial least squares regression (PLSR) method is applied to induce the spatial relationship between the region to be predicted, e.g., STN, and its predictors. The prediction accuracy for validating the invention is evaluated by measuring the shape similarity and the errors in position, size, and orientation between manually segmented STN and its predicted one. | 01-08-2015 |
20150012467 | Computer-Aided Decision Systems - Decision systems and processes implementing digital personas are presented. A digital persona is a digital representation of an entity in accordance with a specific set of rules, preferences, or priorities with respect to a defined situation or opportunity. A digital persona may interact with a universe, which can be a set of conditions and information that an artificial intelligence engine implementing the digital personas can perceive. The digital personas can learn, via the artificial intelligence engine, from actions of a user, events in the universe, other personas, or a multitude of other factors. In some examples discussed, the artificial intelligence engine may include a persona artificial intelligence engine and an evolutionary artificial intelligence engine. | 01-08-2015 |
20150012468 | RECOMMENDER CONTROL SYSTEM, APPARATUS, METHOD AND RELATED ASPECTS - A recommender system ( | 01-08-2015 |
20150012469 | FLIGHT CACHING METHODS AND APPARATUS - According to some aspects, a system is provided comprising at least one computer readable storage medium storing a cache of flight information comprising a plurality of flight solutions, the cache capable of being accessed to obtain flight solutions that meet a criteria specified in one or more flight search queries, and at least one computer programmed to apply at least one machine learning model to at least some of the flight information in the flight information cache to classify at least one of the plurality of flight solutions according to an assessed fidelity of the at least one flight solution, and perform at least one action based on the classified at least one flight solution. | 01-08-2015 |
20150012470 | AUTO-MAINTAINED DOCUMENT CLASSIFICATION - Machines, systems and methods for maintaining a representative data set in a document classification system, the method comprising: including an initial set of seed representative data in a representative data set (RDS) implemented for a knowledge base (KB), wherein the KB is trained to classify documents provided to a document classification system based on analysis of the representative documents included in the RDS and a set of rules, wherein the seed representative data includes a balanced number of representative data across a plurality of classes; updating the RDS by adding or removing representative data from the RDS based on feedback received about accuracy of classification of one or more documents by the classification system; and retraining the KB, wherein the retraining is performed based on occurrence of one or more events. | 01-08-2015 |
20150019463 | ACTIVE FEATURING IN COMPUTER-HUMAN INTERACTIVE LEARNING - A collection of data that is extremely large can be difficult to search and/or analyze. Relevance may be dramatically improved by automatically classifying queries and web pages in useful categories, and using these classification scores as relevance features. A thorough approach may require building a large number of classifiers, corresponding to the various types of information, activities, and products. Creation of classifiers and schematizers is provided on large data sets. Exercising the classifiers and schematizers on hundreds of millions of items may expose value that is inherent to the data by adding usable meta-data. Some aspects include active labeling exploration, automatic regularization and cold start, scaling with the number of items and the number of classifiers, active featuring, and segmentation and schematization. | 01-15-2015 |
20150019464 | method and apparatus for supplying interpolation point data for a data-based function model calculation unit - A method for identifying a set of interpolation point data points from training data for a sparse Gaussian process model, encompassing the following tasks: successively selecting training data points from the set of training data for acceptance into or exclusion from a set of interpolation point data points in accordance with a selection criterion; and terminating selection when a termination criterion exists; the selection criterion depending on a divergence between a target value of the selected training data point and a function value, at the selected training data point, of the Gaussian process model based on the respectively current set of interpolation point data points. | 01-15-2015 |
20150019465 | MITIGATING MEDIA STATION INTERRUPTIONS - A media receiver identifies attributes of a media station to which the media receiver is currently tuned. The media receiver monitors the signal quality of the media station. If the signal quality of the media station drops below a quality threshold, the receiver chooses an alternate station having similar content, and then tunes to the alternate station until the signal quality of the original station improves. When the signal quality of the original station rises above a re-tune threshold, the receiver can switch back to the original station. The receiver can monitor and record user listening patterns, station-switching patterns, and situational parameters, to identify times or locations at which station switches occur. This information can be used to select appropriate alternative stations, to perform pre-emptive station switches, and to determine when a station's content will be buffered with the expectation that a station's signal will drop below the quality threshold. | 01-15-2015 |
20150026101 | IMAGE SEARCH SYSTEM AND METHOD FOR PERSONALIZED PHOTO APPLICATIONS USING SEMANTIC NETWORKS - A system and method for searching a finite collection of images using at least one semantic network. Upon receipt of a query from a user that includes a theme and one or more initial keywords, a set of keywords based on the theme and including the initial keywords is generated from one or more semantic networks corresponding to the theme and/or initial keywords. When the finite collection of images includes suitable metadata, a result set is generated of images corresponding to the expanded set of keywords. When the finite collection includes images lacking in metadata, a remote third-party image collection is searched with the set of keywords to obtain a result set that is used to train visual classifiers as to visual concepts associated with the keywords. The classifiers are used to classify the images in the finite collection lacking metadata and the search of the finite collection is performed with the set of keywords to generate a result set. | 01-22-2015 |
20150026102 | DIRECTORY SERVICE DISCOVERY AND/OR LEARNING - In the context of a client sub-system that requires the use of directory services on behalf of a tenant (such as an overlay tenant), learning an identity of a server node, that can provide such directory services by: (i) sending, by the client sub-system to a first server node, a first directory service request for directory service for a first tenant; (ii) receiving, by the client sub-system, a first acknowledgement from a second server node; and (iii) learning, by the client sub-system, that the second server node can provide directory service for the first tenant based upon the first acknowledgement. | 01-22-2015 |
20150026103 | AUTOMATIC DETECTION OF ANOMALIES IN GRAPHS - A method, apparatus and product for automatic detection of anomalies in graphs. The method comprising obtaining training data, the training data comprising a plurality of graphs, each defined by nodes and edges connecting between the nodes, at least some of the nodes are labeled; determining a statistical model of a graph in accordance with the training data, the statistical model takes into account at least one structured and labeled feature of the graph, wherein the structured and labeled feature of the graph is defined based on a connection between a plurality of nodes and based on at least a portion of the labels of the plurality of nodes; obtaining an examined graph; and determining a score of the examined graph indicative of a similarity between the examined graph and the training data, wherein the score is based on a value of the structured and labeled feature in the examined graph. | 01-22-2015 |
20150026104 | SYSTEM AND METHOD FOR EMAIL CLASSIFICATION - The present invention generally relates to an improved system and method for providing email classification. Specifically, the present invention relates to an email classification system and method for analyzing the signature of an email for proper classification. | 01-22-2015 |
20150026105 | SYSTEMS AND METHOD FOR DETERMINING INFLUENCE OF ENTITIES WITH RESPECT TO CONTEXTS - Systems, methods, and computer-readable media are provided that help advertisers identify and bid for valuable display advertising impressions made available through advertising exchanges. An influence determination system builds an influence graph that includes representations of entities that make advertising impressions available and interactions between such entities. The influence determination system determines contexts relevant to the entities, and applies context labels to entities in the influence graph as appropriate. The influence determination system calculates and stores context-sensitive influence scores for entities in the influence graph. The context labels and context-sensitive influence scores may be used by advertisers to choose advertising impressions on which bids will be placed. | 01-22-2015 |
20150026106 | NON-FACTOID QUESTION-ANSWERING SYSTEM AND COMPUTER PROGRAM - In order to provide a non-factoid question answering system with improved precision, the question answering system ( | 01-22-2015 |
20150026107 | SYSTEM AND APPARATUS THAT IDENTIFIES, CAPTURES, CLASSIFIES AND DEPLOYS TRIBAL KNOWLEDGE UNIQUE TO EACH OPERATOR IN A SEMI-AUTOMATED MANUFACTURING SET-UP TO EXECUTE AUTOMATIC TECHNICAL SUPERINTENDING OPERATIONS TO IMPROVE MANUFACTURING SYSTEM PERFORMANCE AND THE METHODS THEREFOR - A system and method for the capture and storage of industrial process and operational machine data including operator input and environmental factors, the analysis thereof in order to identify elements of tribal knowledge therein, the storage of such elements of tribal knowledge for future reference and analysis and the deployment of such tribal knowledge, specifically in a manufacturing system. | 01-22-2015 |
20150032671 | SYSTEMS AND METHODS FOR SELECTING AND ANALYZING PARTICLES IN A BIOLOGICAL TISSUE - Systems and methods are disclosed for jointly presenting and analyzing morphological characteristics and biomarker expression levels of a biological sample. The systems and methods may utilize a morphological selection component to isolate a population of biological particles in a biological sample for exclusion from further processing. In addition, the systems and methods may simultaneously render morphological and statistical representations of the biological sample on a user interface. | 01-29-2015 |
20150032672 | METHODS, SYSTEMS, AND APPARATUS FOR LEARNING A MODEL FOR PREDICTING CHARACTERISTICS OF A USER - Methods, systems, and apparatus for generating a model for predicting the characteristics of a user are described. A model template for predicting the one or more characteristics of the selected user is obtained. Training data comprising social relationship information and one or more user characteristics for each of one or more source users is obtained. One or more parameters of the model are determined based on the training data. | 01-29-2015 |
20150032673 | Artist Predictive Success Algorithm - Systems and methods are described for training a predictive model using social media data for artists from a period of time prior to the immediate past year and for using the trained model on social media metrics collected in the immediate prior year for the same set of artists to predict probability of success in a future period of time. The “training set” of artists includes both artists that have experienced success in the past year and artists that have yet to experience any success according to selected criteria. The predictive model predicts the next big musical success in the entertainment marketplace. | 01-29-2015 |
20150032674 | PARALLEL DECISION OR REGRESSION TREE GROWING - Embodiments relate to growing a plurality of trees in parallel. An aspect includes creating, for each of a plurality of trees, a data bag based on a training data set comprising a plurality of data records. Another aspect includes splitting the training data set into disjoint data sub-sets; and storing each of the sub-sets in a respective data slice. Another aspect includes performing a single pass through the data records stored in a data slice, thereby identifying one or more of the current nodes that are assigned data records; calculating an intermediate result for each identified current node based on all data records of said data slice; and merging intermediate results into a combined intermediate result. Another aspect includes, for each of the current nodes: calculating a split criterion from the combined intermediate result; and creating two or more child nodes of the current node based on the split criterion. | 01-29-2015 |
20150032675 | SYSTEM AND METHOD FOR MANAGING TARGETED SOCIAL COMMUNICATIONS - A system and method are provided for targeting customers through social networks. Social media data of interest associated with a plurality of social media objects are extracted from at least one social networking platform. The social media data of interest are stored. The social media data are classified according to pre-defined categories. | 01-29-2015 |
20150039538 | METHOD FOR PROCESSING A LARGE-SCALE DATA SET, AND ASSOCIATED APPARATUS - A method for processing at least part of a large-scale dataset, the method comprising: receiving a dataset including a plurality of data points; generating a hash value for at least some of the data points; sorting the generated hash values into a plurality of buckets of identical or substantially identical hash values; generating a similarity matrix for each of the buckets; and applying a machine learning algorithm to the similarity matrices. | 02-05-2015 |
20150039539 | Method and Apparatus For Propagating User Preference Information in a Communications Network - A method for propagating user preference information in a communications network, in which the user preference information may be available for a subset of users within the network. The method comprises generating individual user attribute vectors, based on user historical data and estimating user preference information. The method further comprises defining a community structure for the network and generating a stacked representation for users, the representation comprising the user attribute vector augmented with an aggregated vector of estimated user preferences of members of the user's community. The method further comprises learning a function relating the stacked representation to user preference, using the subset of users, and applying the learned function to users outside the subset. | 02-05-2015 |
20150039540 | METHOD AND APPARATUS FOR EVALUATING PREDICTIVE MODEL - In an approach for evaluating a predictive model, a computer identifies features of training samples in a set of training samples and selects at least one evaluation metric from a set of evaluation metrics as one or more available metrics based on the identified features. The computer applies a predictive model created based on the set of training samples to a set of test samples so as to calculate values of the one or more available metrics and evaluates the predictive model by using the one or more available metrics and the values of the available metrics. With the technical solutions described with respect to the embodiments of the present invention, one or more evaluation metrics that are applicable to specific training sample features may be determined from several evaluation metrics, so that users can precisely evaluate predictive models by using the determined evaluation metrics. | 02-05-2015 |
20150039541 | Feature Extraction and Machine Learning for Evaluation of Audio-Type, Media-Rich Coursework - Conventional techniques for automatically evaluating and grading assignments are generally ill-suited to evaluation of coursework submitted in media-rich form. For courses whose subject includes programming, signal processing or other functionally expressed designs that operate on, or are used to produce media content, conventional techniques are also ill-suited. It has been discovered that media-rich, indeed even expressive, content can be accommodated as, or as derivatives of, coursework submissions using feature extraction and machine learning techniques. Accordingly, in on-line course offerings, even large numbers of students and student submissions may be accommodated in a scalable and uniform grading or scoring scheme. Instructors or curriculum designers may adaptively refine assignments or testing based on classifier feedback. Using developed techniques, it is possible to administer courses and automatically grade submitted work that takes the form of media encodings of artistic expression, computer programming and even signal processing to be applied to media content. | 02-05-2015 |
20150039542 | IMAGE RANKING BASED ON ATTRIBUTE CORRELATION - Images are retrieved and ranked according to relevance to attributes of a multi-attribute query through training image attribute detectors for different attributes annotated in a training dataset. Pair-wise correlations are learned between pairs of the annotated attributes from the training dataset of images. Image datasets may are searched via the trained attribute detectors for images comprising attributes in a multi-attribute query. The retrieved images are ranked as a function of comprising attributes that are not within the query subset plurality of attributes but are paired to one of the query subset plurality of attributes by the pair-wise correlations, wherein the ranking is an order of likelihood that the different ones of the attributes will appear in an image with the paired one of the query subset plurality of attributes. | 02-05-2015 |
20150046376 | SYSTEMS AND METHODS FOR CREATING AN ARTIFICIAL INTELLIGENCE - A computer system implemented method of creating and using artificial intelligence wherein the system includes a first table including at least one textual portion and at least one context phrase contained in the textual portion and the computer system is capable of communication via a network with the user using at least one electronic device, the method comprising: receiving at least one textual input from the user, extracting at least one portion of the textual input from the user and at least one context phrase therefrom, comparing each portion extracted from the textual input from the user to other portions extracted from the textual input from the user according to a first matching algorithm that utilizes the context phrases of each respective portion, and storing in the first table, the portions and respective context phrases that were extracted from the textual input from the user that satisfy the matching algorithm. | 02-12-2015 |
20150046377 | Joint Sound Model Generation Techniques - Joint sound model generation techniques are described. In one or more implementations, a plurality of models of sound data received from a plurality of different sound scenes are jointly generated. The joint generating includes learning information as part of generating a first said model of sound data from a first one of the sound scenes and sharing the learned information for use in generating a second one of the models of sound data from a second one of the sound scenes. | 02-12-2015 |
20150046378 | SYSTEMS AND METHODS FOR UTILIZING MACHINE LEARNING TO IDENTIFY NONTECHNICAL LOSS - Various embodiments of the present disclosure can include systems, methods, and non-transitory computer readable media configured to select a set of signals relating to a plurality of energy usage conditions. Signal values for the set of signals can be determined. Machine learning can be applied to the signal values to identify energy usage conditions associated with non-technical loss. | 02-12-2015 |
20150052087 | Predicting Reactions to Short-Text Posts - This document describes techniques for predicting reactions to short-text posts. In one or more implementations, a prediction model for short-text posts is generated from previous posts to a social network and responses to the posts by the social network community. Subsequently, the prediction model can be used to predict the social network community's reaction to a proposed post prior to the proposed post being posted to the social network. | 02-19-2015 |
20150052088 | Voltage-Based Clustering to Infer Connectivity Information in Smart Grids - Techniques, systems, and articles of manufacture for voltage-based clustering to infer connectivity information in smart grids. A method includes clustering multiple voltage time series measurements into one or more groups, wherein said multiple voltage time series measurements are derived from one or more sensors; determining a connectivity model based on the one or more groups; comparing the determined connectivity model to an existing connectivity model to detect one or more inconsistencies between the determined connectivity model and the existing connectivity model; and updating the existing connectivity model based on said one or more detected inconsistencies. | 02-19-2015 |
20150052089 | INDENTIFYING LOCATIONS OF POTENTIAL USER ERRORS DURING MANIPULATION OF MULTIMEDIA CONTENT - Disclosed is a novel system and method for indicating a probability of errors in multimedia content. The system determines a user state or possible user distraction level. The user distraction level is indicated in the multimedia content. In one example, work is monitored being performed on the multimedia content. Distractions are identified while the work is being monitored. A probability of errors is calculated in at least one location of the multimedia content by on the distractions that have been identified. Annotations are used to indicate of the probability of errors. In another example, the calculating of probability includes using a function F(U,S,P) based on a combination of: i) a determination of user state (U), ii) a determination of sensitivity (S) of user input, and iii) a determination of user characteristics stored in a profile (P). | 02-19-2015 |
20150052090 | SEQUENTIAL ANOMALY DETECTION - A dataset including at least one temporal event sequence is collected. A one-class sequence classifier f(x) that obtains a decision boundary is statistically learned. At least one new temporal event sequence is evaluated, wherein the at least one new temporal event sequence is outside of the dataset. It is determined whether the at least one new temporal event sequence is one of a normal sequence or an abnormal sequence based on the evaluation. Numerous additional aspects are disclosed. | 02-19-2015 |
20150052091 | UNSUPERVISED LEARNING OF ONE DIMENSIONAL SIGNALS - A method for unsupervised learning of one dimensional signals includes obtaining a sample vector from a one dimensional signal and storing the sample vector in a computer accessible memory ( | 02-19-2015 |
20150058264 | METHOD AND SYSTEM OF ITERATIVELY AUTOTUNING PREDICTION PARAMETERS IN A MEDIA CONTENT RECOMMENDER - In one exemplary embodiment, a method of a computerized media-content recommender includes receiving a user-judgment score based on an historical user-listening data with respect to a media content. A first prediction score for a user with respect to the media content is calculated with a media-content recommender. The media-content recommender includes a first set of prediction parameters. A first prediction error including a difference between the user-judgment score and the first prediction score is determined. At least one parameter value of the first set of prediction parameters is modified with a machine-learning optimization technique to generate a second set of prediction parameters. A second prediction score for the user with respect to the media content is calculated with a media-content recommender. A second prediction error including a difference between the user-judgment score and the second prediction score is calculated. | 02-26-2015 |
20150058265 | AUTOMATED SCALING OF MULTI-TIER APPLICATIONS USING REINFORCED LEARNING - A module and method for automatically scaling a multi-tier application, wherein each tier of the multi-tier application is supported by at least one virtual machine, selects one of reinforced learning and heuristic operation based on a policy to recommend a scaling action from a current state of the multi-tier application. If reinforced learning is selected, the reinforced learning is applied to select the scaling action from a plurality of possible actions for the multi-tier application in the current state. If heuristic operation is selected, the heuristic operation is applied to select the scaling action using a plurality of defined heuristics. | 02-26-2015 |
20150058266 | PREDICTIVE ANALYTICS FACTORY - Apparatuses, systems, methods, and computer program products are disclosed for a predictive analytics factory. A function generator module is configured to determine a plurality of learned functions based on training data without prior knowledge regarding suitability of the generated learned functions for the training data. A function evaluator module is configured to perform an evaluation of the plurality of learned functions using test data and to maintain evaluation metadata for the plurality of learned functions. A predictive compiler module is configured to form a predictive ensemble comprising a subset of multiple learned functions from the plurality of learned functions. | 02-26-2015 |
20150066818 | SYSTEMS AND METHODS FOR PREDICTING LOCATION, ONSET, AND/OR CHANGE OF CORONARY LESIONS - Systems and methods are disclosed for predicting the location, onset, or change of coronary lesions from factors like vessel geometry, physiology, and hemodynamics. One method includes: acquiring, for each of a plurality of individuals, a geometric model, blood flow characteristics, and plaque information for part of the individual's vascular system; training a machine learning algorithm based on the geometric models and blood flow characteristics for each of the plurality of individuals, and features predictive of the presence of plaque within the geometric models and blood flow characteristics of the plurality of individuals; acquiring, for a patient, a geometric model and blood flow characteristics for part of the patient's vascular system; and executing the machine learning algorithm on the patient's geometric model and blood flow characteristics to determine, based on the predictive features, plaque information of the patient for at least one point in the patient's geometric model. | 03-05-2015 |
20150066819 | PERIODIC TRAINING FOR UNMATCHED SIGNAL RECEIVER - I/O parameters are adjusted based on a number of errors detected in a received training signal. A controller device sends the training signal while a memory device is in a training mode. The memory device samples the training signal and the system causes an adjustment to at least one I/O parameter based on a detected number of errors. Either the controller or the memory device can perform the error detection, depending on the configuration of the system. Either an I/O parameter of the controller or an I/O parameter of the memory device can be adjusted, depending on the configuration of the system. | 03-05-2015 |
20150066820 | Feature Extraction and Machine Learning for Evaluation of Image-Or Video-Type, Media-Rich Coursework - Conventional techniques for automatically evaluating and grading assignments are generally ill-suited to evaluation of coursework submitted in media-rich form. For courses whose subject includes programming, signal processing or other functionally expressed designs that operate on, or are used to produce media content, conventional techniques are also ill-suited. It has been discovered that media-rich, indeed even expressive, content can be accommodated as, or as derivatives of, coursework submissions using feature extraction and machine learning techniques. Accordingly, in on-line course offerings, even large numbers of students and student submissions may be accommodated in a scalable and uniform grading or scoring scheme. Instructors or curriculum designers may adaptively refine assignments or testing based on classifier feedback. Using developed techniques, it is possible to administer courses and automatically grade submitted work that takes the form of media encodings of artistic expression, computer programming and even signal processing to be applied to media content. | 03-05-2015 |
20150066821 | OBSERVATION VALUE PREDICTION DEVICE AND OBSERVATION VALUE PREDICTION METHOD - A prediction device includes an observation unit configured to obtain an observation value of a target object, a learning unit configured to learn a transition probability and a probability distribution of a model, including the transition probability between a plurality of states and the probability distribution of the observation value which corresponds to each state, from time series data of the observation value, a prediction unit configured to predict a state at a predetermined time based on the transition probability and to predict an observation value corresponding to the state at the predetermined time based on the probability distribution using the time series data of the observation value before the predetermined time. | 03-05-2015 |
20150066822 | SYSTEMS AND METHODS FOR AUTHENTICATING A USER THROUGH AN UNOBSERVABLE RE-AUTHENTICATION SYSTEM - Systems and methods for authenticating a user through an unobservable re-authentication process are disclosed. | 03-05-2015 |
20150066823 | Activating Applications Based on Accelerometer Data - In some implementations, a computer-implemented method includes storing a plurality of acceleration profiles in a mobile device; receiving accelerometer data from an accelerometer in the mobile device; correlating the accelerometer data with one accelerometer profile in the plurality of accelerometer profiles; and activating a user application of the mobile device that is associated with the correlated accelerometer profile. Each acceleration profile can correspond to a sequence of acceleration forces a mobile device would be subjected to when carried with a user during an activity that corresponds to the correlated acceleration profile. | 03-05-2015 |
20150074019 | HEALTH GUIDANCE RECEIVER SELECTION CONDITION GENERATION SUPPORT DEVICE - A memory that stores health checkup data of a person and a label value representing whether or not the person fell under a predetermined health guidance criterion in the subsequent period, and a processor connected with the memory are provided. The processor learns a discriminant model with use of the health checkup data of each person and the label value. The discriminant model, in which health checkup items of the health checkup data are used as explanatory variables, is represented as a polynomial including the explanatory variables and coefficients of the respective explanatory variables, and is used for discriminating whether or not the person falls under the health guidance criterion in the subsequent period. The processor generates, as a selection condition, combinations of the health checkup items as the explanatory variables and values of the coefficients in the discriminant model after learning. | 03-12-2015 |
20150074020 | SENTIMENT POLARITY FOR USERS OF A SOCIAL NETWORKING SYSTEM - A social networking system infers a sentiment polarity of a user toward content of a page. The sentiment polarity of the user is inferred based on received information about an interaction between the user and the page (e.g., like, report, etc.), and may be based on analysis of a topic extracted from text on the page. The system infers a positive or negative sentiment polarity of the user toward the content of the page, and that sentiment polarity then may be associated with any second or subsequent interaction from the user related to the page content. The system may identify a set of trusted users with strong sentiment polarities toward the content of a page or topic, and may use the trusted user data as training data for a machine learning model, which can be used to more accurately infer sentiment polarity of users as new data is received. | 03-12-2015 |
20150074021 | GENERATING A TRAINING MODEL BASED ON FEEDBACK - A method and apparatus for generating a training model based on feedback are provided. The method for generating a training model based on feedback, includes calculating an eigenvector of a sample among a plurality of samples; obtaining scores granted by a user for one or more of the plurality of samples in a round, obtaining scores granted by the user for a first number of samples; obtaining scores granted by the user for a second number of samples in response to detecting, based on the eigenvector, an inconsistency between the scores granted by the user for the first number of samples; and generating a training model based on the scores granted by the user for the first and second numbers of samples. A corresponding apparatus is also provided. | 03-12-2015 |
20150074022 | METHOD AND SYSTEM OF AUTOMATICALLY DOWNLOADING MEDIA CONTENT IN A PREFERRED NETWORK - In one exemplary aspect, a sorted list of scored media content episodes is received with a computing device of a user. Each respective media content episode is scored by an iterative autotuning prediction algorithm, and wherein each element of the sorted list of scored media content episodes comprises a value that represents a likelihood of a user listening to the respective media content episode and a reference to a location of the respective media content episode. A number of bytes of a download iteration for each media content episode is determined based on value that represents a likelihood of the user listening to the respective media content episode and an index of the respective media content episode in the sorted list. It is detected that a mobile device is in the preferred network. The download iteration is implemented for each media content episode when it is detected that the mobile device is in the preferred network. | 03-12-2015 |
20150074023 | UNSUPERVISED BEHAVIOR LEARNING SYSTEM AND METHOD FOR PREDICTING PERFORMANCE ANOMALIES IN DISTRIBUTED COMPUTING INFRASTRUCTURES - An unsupervised behavior learning system and method for predicting anomalies in a distributed computing infrastructure. The distributed computing infrastructure includes a plurality of computer machines. The system includes a first computer machine and a second computer machine. The second computer machine is configured to generate a model of normal and anomalous behavior of the first computer machine, where the model is based on unlabeled training data. The second computer machine is also configured to acquire real-time data of system level metrics of the first machine; determine whether the real-time data is normal or anomalous based on a comparison of the real-time data to the model; and predict a future failure of the first computer machine based on multiple consecutive comparisons of the real-time data to the model. Upon predicting a future failure of the first computer machine, generate a ranked set of system-level metrics which are contributors to the predicted failure of the first computer machine, and generate an alarm that includes the ranked set of system-level metrics. The model of normal and anomalous behavior may include a self-organizing map. | 03-12-2015 |
20150074024 | Inverse Function Method of Boolean Satisfiability (SAT) - A computer system uses an inverse function method to solve Boolean Satisfiability problems. The system benefits from the system disclosed in the US patent “Knowledge Acquisition and Retrieval Apparatus and Method” (U.S. Pat. No. 6,611,841). The system applies a learning function to access iterative set relations among variables, literals, words and clauses as knowledge; and applies deduction and reduction functions to retrieve relations as reasoning. The system uses knowledge learning (KL) and knowledge reasoning algorithms (KRA). The system abandons the “OR” operation of Boolean logic and processes only set relations on data. The system leverages the reversibility of deduction and reduction to determine whether 3-SAT formulas are satisfiable. | 03-12-2015 |
20150081598 | GENERATING APPLICATION MODELS BASED ON DISCOVERY BASED MACHINE LEARNING - Embodiments are directed towards generating application models based on discovery based machine learning. A mobile application may be uploaded to a computer that may be part of a testing platform. A reference mobile computer may be selected and the mobile application maybe installed onto the reference mobile computer. Also, the testing platform may generate an initial application model based on the mobile application. The current active window of the mobile application may be determined and the application model may be updated accordingly. Screenshots may be generated that correspond to each current active window of the mobile application. Also, each user-interface control in the active window may be activated. The results of activating each control may be observed and added to the model. If the activation causes navigation, another active window may be determined. The application model may be used for testing other mobile computers. | 03-19-2015 |
20150081599 | Method, Apparatus and Computer Program Product for Determining Failure Regions of an Electrical Device - One or more failure regions are determined for an electrical device by training a machine learning classifier, including analyzing data points for the device and recognizing patterns in the data points. Each data point indicates pass or fail of the device for a particular combination of factors relating to the operation of the device. The trained machine learning classifier is used to predict the pass/fail state of new data points for the electrical device. Each new data point corresponds to a new combination of the factors relating to the operation of the device not previously analyzed by the machine learning classifier. A pass/fail border region can be identified for the electrical device based on the training of the machine learning classifier, the pass/fail border region excluding data points for which the electrical device is expected to pass or fail with a high degree of certainty. | 03-19-2015 |
20150081600 | Nonlinear Classification of Data - The present invention relates to a method for nonlinear classification of high dimensional data by means of boosting, whereby a target class with significant intra-class variation is classified against a large background class, where the boosting algorithm produces a strong classifier, the strong classifier being a linear combination of weak classifiers. The present invention specifically teaches that weak classifiers classifiers h | 03-19-2015 |
20150081601 | AUTOMATIC GENERATION OF PREFERRED VIEWS FOR PERSONAL CONTENT COLLECTIONS - Providing a view of relevant items of a content collection includes identifying a current context based temporal parameters, spatial parameters, navigational parameters, lexical parameters, organizational parameters, and/or events, evaluating each of the items of the content collection according to the current context to provide a value for each of the items, and displaying a subset of the items corresponding to highest determined values. The temporal parameters may include a time of recent access of an item, frequency of access of an item, frequency of location related access of an item, and frequency of event related access of an item. Temporal patterns of accessing items may be numerically assessed based on time of day, time of week, and/or time of month. Evaluating each item may include determining a distance from a separating hyperplane using a support vector machine classification method. | 03-19-2015 |
20150081602 | Apparatus and Method to Increase Accuracy in Individual Attributes Derived from Anonymous Aggregate Data - An apparatus and method to increase accuracy in individual attributes derived from anonymous aggregate data uses aggregation keys in order to retrieve distribution sets and generate best-effort results for individual attributes. Multiple aggregation keys may be utilized to which individual attributes may be cross-mapped. The aggregation keys may be divided into location-based aggregation keys and name-based aggregation keys. The resulting data may be of varying granularity depending upon the granularity of the aggregation key used for the distribution and to generate the attributes. | 03-19-2015 |
20150081603 | Systems and Methods to Facilitate Local Searches Via Location Disambiguation - Systems and methods use machine learning techniques to resolve location ambiguity in search queries. In one aspect, a dataset generator generates a training dataset using query logs of a search engine. A training engine applies a machine learning technique to the training dataset to generate a location disambiguation model. A location disambiguation engine uses the location disambiguation model to resolve location ambiguity in subsequent search queries. | 03-19-2015 |
20150081604 | Video Content Analysis For Automatic Demographics Recognition Of Users And Videos - A demographics analysis trains classifier models for predicting demographic attribute values of videos and users not already having known demographics. In one embodiment, the demographics analysis system trains classifier models for predicting demographics of videos using video features such as demographics of video uploaders, textual metadata, and/or audiovisual content of videos. In one embodiment, the demographics analysis system trains classifier models for predicting demographics of users (e.g., anonymous users) using user features based on prior video viewing periods of users. For example, viewing-period based user features can include individual viewing period statistics such as total videos viewed. Further, the viewing-period based features can include distributions of values over the viewing period, such as distributions in demographic attribute values of video uploaders, and/or distributions of viewings over hours of the day, days of the week, and the like. | 03-19-2015 |
20150088789 | HIERARCHICAL LATENT VARIABLE MODEL ESTIMATION DEVICE, HIERARCHICAL LATENT VARIABLE MODEL ESTIMATION METHOD, SUPPLY AMOUNT PREDICTION DEVICE, SUPPLY AMOUNT PREDICTION METHOD, AND RECORDING MEDIUM - A hierarchical latent structure setting unit | 03-26-2015 |
20150088790 | HYBRID SYSTEM FOR DEMAND PREDICTION - In demand prediction, a history of demand for a resource is modeled to generate a baseline model of the demand, and demand for the resource at a prediction time is predicted by evaluating a regression function of depth k operating on an input data set including at least the demand for the resource at the prediction time output by the baseline model and measured demand for the resource measured at k times prior to the prediction time. The resource may be off-street parking, and the input data set may further include weather data. The regression function may comprise a support vector regression (SVR) function that is trained on the history of demand for the resource. The baseline model suitably comprises a Fourier model of the history of demand for the resource. | 03-26-2015 |
20150088791 | GENERATING DATA FROM IMBALANCED TRAINING DATA SETS - Injecting generated data samples into a minority data class of an imbalanced training data set is provided. In response to receiving an input to balance the imbalanced training data set that includes a majority data class and the minority data class, a set of data samples is generated for the minority data class. A distance is calculated from each data sample in the set of generated data samples to a center of a kernel that includes a set of data samples of the majority data class. Each data sample in the set of generated data samples is stored within a corresponding distance score bucket based on the calculated distance of a data sample. Generated data samples are selected from a number of highest ranking distance score buckets. The generated data samples selected from the number of highest ranking distance score buckets are injected into the minority data class. | 03-26-2015 |
20150088792 | Learning Geofence Models Directly - Methods and apparatus are directed to geofencing applications that utilize machine learning. A computing device can receive a plurality of geofence-status indications, where a geofence-status indication includes training data associated with a geofence at a first location. The geofence is associated with a geographical area. The computing device trains a geofence-status classifier to determine a geofence status by providing the training data as input to the geofence-status classifier. The training data includes data for a plurality of training features. After the geofence-status classifier is trained, the computing device receives query data associated with a second location. The query data includes data for a plurality of query features. The query features include a query feature that corresponds to a training feature. The query data is input to the geofence-status classifier. After providing the query data, the trained geofence-status classifier indicates the geofence status. | 03-26-2015 |
20150088793 | SKILLS ONTOLOGY CREATION - Disclosed in some examples are systems, methods, and machine readable mediums which allow for the automatic creation of a skills hierarchy. The skills hierarchy comprises an organization of a standardized list of skills into a hierarchy that describes category relationships between the skills in the hierarchy. The category relationships may include no relationships, parent relationships, and child relationships. A skill may be considered a parent of another skill if the parent skill describes a broader category of skill that includes the child. Other relationships such as grandparent (e.g., a parent's parent), great-grandparent, grandchild, great grandchild and so on may be defined inferentially as well. In some examples, the constructed hierarchy may be organized with broader skills at higher levels and narrower skills at lower levels. | 03-26-2015 |
20150088794 | METHODS AND SYSTEMS OF SUPERVISED LEARNING OF SEMANTIC RELATEDNESS - A method of evaluating a semantic relatedness of terms. The method comprises providing a plurality of text segments, calculating, using a processor, a plurality of weights each for another of the plurality of text segments, calculating a prevalence of a co-appearance of each of a plurality of pairs of terms in the plurality of text segments, and evaluating a semantic relatedness between members of each the pair according to a combination of a respective the prevalence and a weight of each of the plurality of text segments wherein a co-appearance of the pair occurs. | 03-26-2015 |
20150095270 | SYSTEMS AND METHODS FOR AUTOMATED AND REAL-TIME DETERMINATION OF OPTIMUM INFORMATION HANDLING SYSTEM LOCATION - In accordance with embodiments of the present disclosure, a system may comprise a plurality of slots each configured to receive a modular information handling system, a plurality of air movers each configured to cool at least one modular information handling system disposed in at least one of the plurality slots, and a chassis management controller communicatively coupled to the plurality of slots and the plurality of air movers and configured to display a recommended placement of modular information handling systems in the plurality of slots based on at least one of: identities of slots populated with modular information handling systems, an airflow ranking of the plurality of slots, an impedance ranking of information handling systems disposed in the slots, and a workload of each of the information handling systems disposed in the slots. | 04-02-2015 |
20150095271 | METHOD AND APPARATUS FOR CONTEXTUAL LINEAR BANDITS - A method of selection that maximizes an expected reward in a contextual multi-armed bandit setting gathers rewards from randomly selected items in a database of items, where the items correspond to arms in a contextual multi-armed bandit setting. Initially, an item is selected at random and is transmitted to a user device which generates a reward. The items and resulting rewards are recorded. Subsequently, a context is generated by the user device which causes a learning and selection engine to calculate an estimate for each arm in the specific context, the estimate calculated using the recorded items and resulting rewards. Using the estimate, an item from the database is selected and transferred to the user device. The selected item is chosen to maximize a probability of a reward from the user device. | 04-02-2015 |
20150095272 | ESTIMATION OF PREDICTIVE ACCURACY GAINS FROM ADDED FEATURES - Various technologies described herein pertain to estimating predictive accuracy gain of a potential feature added to a set of features, wherein an existing predictor is trained on the set of features. Outputs of the existing predictor for instances in a dataset can be retrieved from a data store. Moreover, a predictive accuracy gain estimate of a potential feature added to the set of features can be measured as a function of the outputs of the existing predictor for the instances in the dataset. The predictive accuracy gain estimate can be measured without training an updated predictor on the set of features augmented by the potential feature. | 04-02-2015 |
20150100524 | SMART SELECTION OF TEXT SPANS - A text span forming either a single word or a series of two or more words that a user intended to select is predicted. A document and a location pointer that indicates a particular location in the document are received and input to different candidate text span generation methods. A ranked list of one or more scored candidate text spans is received from each of the different candidate text span generation methods. A machine-learned ensemble model is used to re-score each of the scored candidate text spans that is received from each of the different candidate text span generation methods. The ensemble model is trained using a machine learning method and features from a dataset of true intended user text span selections. A ranked list of re-scored candidate text spans is received from the ensemble model. | 04-09-2015 |
20150100525 | METHOD AND SYSTEM FOR THE DETECTION OF ANOMALOUS SEQUENCES IN A DIGITAL SIGNAL - Method and system for the detection of anomalous behavior in systems displaying typical and complex behavior encoded in a digital signal through the study of a computational model (artificial system) of interacting agents defined using the information contained in the digital signal and imposing that agents engage in a maximally frustrated dynamics. Changes in the target system's behavior lead a measurable decrease in frustration of the artificial system, from sequences never presented before during the system's normal behavior or combinations of already presented sequences never seen together. | 04-09-2015 |
20150100526 | ONLINE TEMPORAL DIFFERENCE LEARNING FROM INCOMPLETE CUSTOMER INTERACTION HISTORIES - In one embodiment, an indication that a decision has been requested, selected, or applied with respect to one or more users may be obtained. After the indication that a decision that has been requested, selected, or applied is obtained, a value function may be updated, where the value function approximates an expected reward associated with the one or more users over time since the decision has been requested, selected, or applied with respect to the one or more users. The value function may be updated by performing or providing one or more updates to the value function, where a time at which each of the one or more updates is performed or provided is independent of activity of the one or more users. | 04-09-2015 |
20150106308 | DISTRIBUTED MACHINE LEARNING INTELLIGENCE DEVELOPMENT SYSTEMS - A system, method, and computer-readable instructions for a distributed machine learning system are provided. A plurality of distributed learning environments are in communication over a network, wherein each environment has a computing device having a memory and a processor coupled to the memory, the processor adapted implement a learning environment via one or more agents in a rules-based system, wherein the agents learn to perform tasks in their respective learning environment; and a persistent storage in which knowledge comprising a plurality of rules developed by the agents for performing the tasks are stored, wherein the knowledge is tagged and shared with other agents throughout the plurality of distributed learning environments. | 04-16-2015 |
20150112896 | Cross-Channel Content Translation Engine - An embodiment according to the present invention addresses reusability and alignment of content across channels in a multi-channel virtual assistant, by allowing users to define content on one channel and then have the content fully or partially translated for the other channels using a mix of pre-defined static rules, dynamic rules or machine learning. Content translation is provided based on communications channels, and content translation is performed from one to many formats, optionally in real time. Performing content translation using machine learning provides an advantage that as users work, content translation becomes more precise and covers more elements. | 04-23-2015 |
20150112897 | Method for Estimating Parameters of a Graph Spectral Filter Using Training Data - A method processes a signal represented as a graph by first determining a graph spectral transform based on the graph. In a spectral domain, parameters of a graph filter are estimated using a training data set of unenhanced and corresponding enhanced signals. The graph filter is derived based on the graph spectral transform and the estimated graph filter parameters. Then, the signal is processed using the graph filter to produce an output signal. The processing can enhance signals such as images b denoising or interpolating missing samples. | 04-23-2015 |
20150112898 | SITE FLOW OPTIMIZATION - A method and system to present an optimum action in response to a flow of actions in a computer network from a user are provided. For each of a plurality of possible presented actions corresponding to a particular flow of actions in a computer network, and for each of one or more possible performed actions for each possible presented action, a likelihood that a user will perform the possible performed action is determined. Then each of the determined likelihoods is weighted by applying a weight assigned to a corresponding possible presented action. An optimum presented action is identified determining a presented action having a weighted maximum determined likelihood, based on the weighted determined likelihood. | 04-23-2015 |
20150112899 | METHOD AND SYSTEM FOR ASSESSMENT OF COGNITIVE FUNCTION BASED ON ELECTRONIC DEVICE USAGE - A system and method that enables a person to unobtrusively quantify the effect of mobility, physical activity, learning, social interaction and diet on cognitive function. The method records on the electronic device one of global positioning system longitude and latitude coordinates, accelerometer coordinates, and gyroscope coordinates, one of outgoing and incoming phone calls, outgoing and incoming emails, and outgoing and incoming text messages, one of URLs visited on an internet browser application, books read on an e-reader application, games played on game applications, and the nutritional content of food consumed, performs the step of learning a function mapping from those recordings to measurements of cognitive function using a loss function to identify a set of optimal weights that produce a minimum for the loss function, uses those optimal weights to create the function mapping, and performs the step of computing the variance of the cognitive function measurements that is explained by the function mapping to assign an attribution to the effect of physical activity on measured changes in cognitive function. | 04-23-2015 |
20150112900 | TIME-SERIES DATA PREDICTION DEVICE, TIME-SERIES DATA PREDICTION METHOD, AND PROGRAM - A time-series data prediction device includes an acquisition unit, a prediction model generation unit, and a prediction unit. The acquisition unit acquires a plurality of observation values that continue at predetermined time intervals, as a prediction data, from time-series data of an observation value of a predetermined observation target, and acquires a training data. The prediction model generation unit generates a prediction model to calculate time-series data, which is an observation value predicted based on given time-series data, using the training data. The prediction unit calculates a predicted value of an observation value using the generated prediction model and the prediction data. | 04-23-2015 |
20150112901 | MACHINE LEARNING SYSTEM FOR ASSESSING HEART VALVES AND SURROUNDING CARDIOVASCULAR TRACTS - A machine learning system for evaluating at least one characteristic of a heart valve, an inflow tract, an outflow tract or a combination thereof may include a training mode and a production mode. The training mode may be configured to train a computer and construct a transformation function to predict an unknown anatomical characteristic and/or an unknown physiological characteristic of a heart valve, inflow tract and/or outflow tract, using a known anatomical characteristic and/or a known physiological characteristic the heart valve, inflow tract and/or outflow tract. The production mode may be configured to use the transformation function to predict the unknown anatomical characteristic and/or the unknown physiological characteristic of the heart valve, inflow tract and/or outflow tract, based on the known anatomical characteristic and/or the known physiological characteristic of the heart valve, inflow tract and/or outflow tract. | 04-23-2015 |
20150112902 | INFORMATION PROCESSING DEVICE - The information processing device according to one embodiment comprises a storage device for storing model information generated through execution of machine learning while employing learning data, the model information including feature information and weighting information associated with the feature information, for each of a plurality of labels; and a display control device for displaying the feature information included in the model information for at least one label among the plurality of labels on a display device, on the basis of the weighting information associated with the feature information. | 04-23-2015 |
20150112903 | DEFECT PREDICTION METHOD AND APPARATUS - Embodiments of the present invention disclose a defect prediction method and apparatus, which relate to the data processing field, and implement accurate and quick locating of a defect in a faulty product. A specific solution is as follows: selecting a training attribute set from a pre-stored product fault record according to a target attribute, and combining the target attribute and the training attribute set into a training set, where the target attribute is a defect attribute of a historical faulty product; generating a classifier set according to the training set, where the classifier set includes at least two tree classifiers; and predicting a defect of a faulty product by using the classifier set as a prediction model. The present invention is used in a process of predicting a defect of a faulty product. | 04-23-2015 |
20150120619 | SOCIAL COLLABORATION IN PROBABILISTIC PREDICTION - A method, system, and computer program product for social collaboration in probabilistic prediction are provided in the illustrative embodiments. A set of predictions is sent to a user device. A prediction in the set of predictions is a probability of an outcome of an event. The probability is computed using a prediction model trained with training data corresponding to the event. An input is received from the user device. The input comprises a new prediction made at the user device using a new prediction model executing on the user device. A difference is determined between the prediction and the new prediction. The prediction model is revised to produce a revised prediction. A revised difference between the revised prediction and the new prediction is smaller than the difference. | 04-30-2015 |
20150120620 | SYSTEMS AND METHODS FOR ASSESSING ALIGNMENT OF AN ENTITY - The present invention relates to a systems, methods, and articles of manufacture for enabling assessment of alignment of an entity. In one embodiment, content associated with the entity and comprising text, and responses to questions presented to stakeholders of the entity are received and processed to derive data sets of contextually significant words or phrases for the content and responses respectively. The contextually significant words or phrases of the respective data sets are correlated and output. | 04-30-2015 |
20150120621 | Dynamic Load Balancing Based on Question Difficulty - Mechanisms are provided for performing load balancing of question processing in a Question and Answer (QA) system, implemented by the data processing system, having a plurality of QA system pipelines. The mechanisms receive an input question for processing by the QA system and determine a predicted question difficulty of the input question. The mechanisms select a QA system pipeline from the plurality of QA system pipelines based on the predicted question difficulty and route the input question to the selected QA system pipeline for processing. In addition, the mechanisms process the input question with the selected QA system pipeline to generate an answer for the input question. | 04-30-2015 |
20150120622 | SWITCHING SYSTEM, LINE CARD, SWITCH CARD, FDB LEARNING METHOD, FDB LEARNING ARBITRATION METHOD AND PROGRAM - A switching system includes a plurality of line cards and a switch card. The line card or cards notifies the switch card about whether or not the line card or cards is in the FDB learning enabled state. The switch card includes a learning information storage unit that holds in store the FDB learning information received from the multiple line cards and an FDB learning arbitration unit that, when all of the line cards are in an FDB learning enabled state, selects and sends the FDB learning information, stored in the learning information storage unit, to the respective line cards, based on a notification from each of the line cards. | 04-30-2015 |
20150120623 | Method of Analyzing a Graph With a Covariance-Based Clustering Algorithm Using a Modified Laplacian Pseudo-Inverse Matrix - A covariance-clustering algorithm for partitioning a graph into sub-graphs (clusters) using variations of the pseudo-inverse of the Laplacian matrix (A) associated with the graph. The algorithm does not require the number of clusters as an input parameter and, considering the covariance of the Markov field associated with the graph, algorithm finds sub-graphs characterized by a within-cluster covariance larger than an across-clusters covariance. The covariance-clustering algorithm is applied to a semantic graph representing the simulated evidence of multiple events. | 04-30-2015 |
20150120624 | APPARATUS AND METHOD FOR INFORMATION PROCESSING - Provided is an information processing apparatus including a sorting unit configured to sort a second data set as evaluation data with a sorter generated by learning through supervised learning that uses a first data set as teacher data, an input unit configured to receive label correction for the second data set in accordance with a sorting result from the sorting unit, and an update unit configured to update the second data set to reflect the correction received by the input unit. | 04-30-2015 |
20150120625 | SYSTEM FOR EXTRACTING CUSTOMER FEEDBACK FROM A MICROBLOG SITE - A system for extracting customer feedback from a microblog site includes a retrieval unit coupled to the microblog site to capture microblog updates. A filter unit coupled to the retrieval unit filters the captured microblog updates according to filter criteria that remove non-actionable items from the captured microblog updates. A learning unit coupled to the filter unit prioritizes the filtered microblog updates, and a classification unit coupled to the learning unit classifies the filtered and prioritized microblog updates. An action unit coupled to the classification unit performs appropriate actions based on the classified, filtered and prioritized microblog updates. | 04-30-2015 |
20150127588 | PRUNING PROCESS EXECUTION LOGS - Methods and systems for pruning process execution logs include learning a predictive model from a set of execution traces that characterize a process, where the predictive model determines a likelihood of a given instance reaching a specified outcome; identifying attributes in the predictive model that fall below a threshold measure of relevance to the specified outcome using a processor; and removing the identified attributes from the set of execution traces. | 05-07-2015 |
20150127589 | ITERATIVE REFINEMENT OF PATHWAYS CORRELATED WITH OUTCOMES - A method for refining a process model includes mining a process model from a set of execution traces; determining whether the process model is too dense or too sparse; learning a predictive model from the execution traces to predict an outcome; modifying the predictive model; and mining a refined process model from updated traces based on attributes present in the modified predictive model. Modifying the predictive model includes making the predictive model more specific if it is determined that the process model is too dense; and making the predictive model more general if it is determined that the process model is too sparse. | 05-07-2015 |
20150127590 | SYSTEMS AND METHODS FOR LAYERED TRAINING IN MACHINE-LEARNING ARCHITECTURES - A computer-implemented method for layered training of machine-learning architectures includes receiving a plurality of data elements wherein each data element is associated with a timestamp, determining a training window for each model layer of a layered stack of model layers, determining a plurality of training data elements for each training window by identifying the data elements with timestamps corresponding to each of the training windows, identifying a previous checkpoint for each model layer wherein the previous checkpoint for each model layer is generated by a parent model layer, training each model layer with the determined training data elements for each model layer and the identified previous checkpoint for each model layer, generating a plurality of current checkpoints wherein each current checkpoint of the plurality of current checkpoints is associated with a model layer, and storing the plurality of current checkpoints at the memory. | 05-07-2015 |
20150127591 | IDENTIFYING SUGGESTIVE INTENT IN SOCIAL POSTS - This document describes techniques for identifying suggestive intent in social posts. In one or more implementations, a topic is received and social posts to one or more social networks that are related to the topic are collected. Then, one or more suggestive intent posts expressing suggestions towards the topic are identified from the collected social posts. In one or more implementations, a set of related topics are received and social posts to one or more social networks that correspond to the related topics are collected. Then, aspects corresponding to the related topics are identified, and a sentiment score is generated for each aspect of each related topic that can be used to compare aspects of the related topics. In one or more implementations a suggestive intent model, usable to identify social posts expressing suggestive intent, is built from a training corpus of annotated social posts. | 05-07-2015 |
20150127592 | INTERACTIVE CLOTHES SEARCHING IN ONLINE STORES - A clothing search system provides a clothing search to users using a component-based image search. Retailer catalogs are analyzed to determine clothing components within clothing images. Features associated with the components are determined. When a user requests a clothing search, the clothing search system selects clothing based on the components and features requested by the user. The user may also provide an image to the clothing search system. The clothing search system determines components and features of the image and identifies clothing with matching components. | 05-07-2015 |
20150127593 | Platform to Acquire and Represent Human Behavior and Physical Traits to Achieve Digital Eternity - An artificial intelligence platform that is capable of reproducing a person's identity and allowing others to interact with it is described. It does so by creating a Digital Identity, founded on the very concept of a Digital Soul capable of bringing back to life (mirroring) the physical aspect, behavior, emotions, mannerisms and relational sphere of the subject. Each Digital Identity is capable of interacting with its surroundings and of formulating specific responses based on an innovative knowledge base structure of the individual, his emotional background (psychological model) and relational structure (skills/aptitude). The creation of a Digital Identity is defined by the ensemble of the individual's Digital Soul (Animus, Loquor and Indoles), physical characteristics (Corpus) and relational sphere with people (Societas) and with the environment (Circum). | 05-07-2015 |
20150134575 | USING DATA OBTAINED FROM AN ONLINE SOCIAL NETWORK TO OPTIMIZE SUBSCRIPTION OFFERS - The disclosed embodiments relate to a system that uses data from an online social network to optimize subscription offers. During operation of the online social network, the system gathers data associated with subscription offers that were presented to members of the online social network, including information about which subscription offers were converted. Next, the system uses a machine-learning technique to train a model based on the gathered data. Finally, the system uses the trained model to select subscription offers to present to a member of the online social network. | 05-14-2015 |
20150134576 | MEMORY FACILITATION USING DIRECTED ACYCLIC GRAPHS - Memory facilitation using directed acyclic graphs is described, for example, where a plurality of directed acyclic graphs are trained for gesture recognition from human skeletal data, or to estimate human body joint positions from depth images for gesture detection. In various examples directed acyclic graphs are grown during training using a training objective which takes into account both connection patterns between nodes and split function parameter values. For example, a layer of child nodes is grown and connected to a parent layer of nodes using an initialization strategy. In examples, various local search processes are used to find good combinations of connection patterns and split function parameters. | 05-14-2015 |
20150134577 | SYSTEM AND METHOD FOR PROVIDING INFORMATION - There is provided a system for providing information. The system includes a data classifying device configured to receive original data and classify the original data as real time data or general data; a real time data analyzing device configured to receive the real time data from the data classifying device and generate condensed information including only a part that satisfies predefined conditions among attribute information of the real time data; and a distributed parallel processing device configured to receive the general data from the data classifying device, perform a predetermined distributed parallel computation process on the general data, and generate analysis information. | 05-14-2015 |
20150134578 | DISCRIMINATOR, DISCRIMINATION PROGRAM, AND DISCRIMINATION METHOD - A discriminator based on supervised learning includes a data expanding unit and a discriminating unit. The data expanding unit performs data expansion on unknown data which is an object to be discriminated in such a manner that a plurality of pieces of pseudo known data are generated. The discriminating unit applies the plurality of pieces of unknown pseudo data that has been expanded by the data expansion unit to a discriminative model so as to discriminate the plurality of pieces of pseudo unknown data, and integrates discriminative results of the plurality of pieces of pseudo unknown data to perform class classification such that the unknown data is classified into classes. | 05-14-2015 |
20150134579 | INFORMATION PROPAGATION PROBABILITY FOR A SOCIAL NETWORK - One or more techniques and/or systems are disclosed for predicting propagation of a message on a social network. A predictive model is trained to determine a probability of propagation of information on the social network using both positive and negative information propagation feedback, which may be collected while monitoring the social network over a desired period of time for information propagation. A particular message can be input to the predictive model, which can determine a probability of propagation of the message on the social network, such as how many connections may receive at least a portion of the message and/or a likelihood of at least a portion of the message reaching respective connections in the social network. | 05-14-2015 |
20150142707 | METHOD AND SYSTEM FOR CLUSTERING, MODELING, AND VISUALIZING PROCESS MODELS FROM NOISY LOGS - A process discovery system that includes an offline system training module configured to cluster similar process log traces using Non-negative Matrix Factorization (NMF) with each cluster representing a process model, and learn a Conditional Random Field (CRF) model for each process model and an online system usage module configured to decode new incoming log traces and construct a process graph in which transitions are shown or hidden according to a tuning parameter. | 05-21-2015 |
20150142708 | RETRIEVAL OF SIMILAR IMAGES TO A QUERY IMAGE - Methods, systems, and articles of manufacture for annotating of an image are disclosed. These include scoring the image using a plurality of trained classifiers, wherein each of the trained classifiers corresponds to at least one of a plurality of image groups clustered based upon image similarity, and wherein each image group is associated with a set of weighted labels; selecting one or more of the image groups based upon the scoring; aggregating one or more sets of weighted labels associated with the selected one or more image groups; and annotating the image using the aggregated one or more sets of weighted labels. | 05-21-2015 |
20150142709 | AUTOMATIC LEARNING OF BAYESIAN NETWORKS - A method of learning a structure of a Bayesian network includes computing an ordering of the random variables of the Bayesian network; wherein computing the ordering of the random variables of the Bayesian network is performed by computing an approximate solution to the history dependent traveling salesman problem. | 05-21-2015 |
20150142710 | Directed Behavior in Hierarchical Temporal Memory Based System - A hierarchy of computing modules is configured to learn a cause of input data sensed over space and time, and is further configured to determine a cause of novel sensed input data dependent on the learned cause. At least one of the computing modules has a sequence learner module configured to associate sequences of input data received by the computing module to a set of causes previously learned in the hierarchy. | 05-21-2015 |
20150142711 | INTERESTINGNESS RECOMMENDATIONS IN A COMPUTING ADVICE FACILITY - The present disclosure provides a recommendation to a user through a computer-based advice facility, comprising collecting topical information, wherein the collected topical information includes an interestingness aspect; filtering the collected topical information based on the interestingness aspect; determining an interestingness rating from the collected topical information, wherein the determining is through the computer-based advice facility; and providing a user with the recommendation related to the topical information based on the interestingness rating. | 05-21-2015 |
20150149393 | Predictive Computer System Resource Monitoring - A method, system and computer-usable medium are disclosed for monitoring a computer system to predict failed or degraded operational states and respond with an alarm or corrective action. Resource collection and consumption are analyzed to derive velocity and acceleration. A hidden Markov model with the resource collection and consumption data as observation spaces predicts computer system state spaced indicative of a failed or degraded computer system operating state. | 05-28-2015 |
20150149394 | Boundary Graph Machine Learning Algorithm for Regression and Classification - There is provided a system and method for training and utilizing a boundary graph machine learning algorithm. The system including a processor configured to receive a plurality of entry nodes, each of the plurality of entry nodes including an entry node input and an entry node output, add each of the plurality of entry nodes to a graph using the entry node input and the entry node output, receiving a plurality of training nodes, each of the plurality of training nodes including a training node input and a training node output, add each of the plurality of training nodes to the graph when the training node input for each of the plurality of training nodes is similar to the training node output of a closest node and the training node output of each of the plurality of training nodes is different than the training node output of the closest node. | 05-28-2015 |
20150293917 | Confidence Ranking of Answers Based on Temporal Semantics - A mechanism is provided, in a data processing system comprising a processor and a memory configured to implement a question and answer system (QA), for providing confidence rankings based on temporal semantics. Responsive to receiving an input question, a set of candidate answers is identified from a knowledge domain based on a correlation between an identified one or more predicates and an identified one or more arguments to the knowledge domain. A confidence score is associated with each of the candidate answers and each confidence score associated with each candidate answer is refined based on a set of temporal characteristics identified in the input question. A set of temporally refined candidate answers is then provided to the user. | 10-15-2015 |
20150294220 | STRUCTURING DATA AROUND A TOPICAL MATTER AND A.I./N.L.P./ MACHINE LEARNING KNOWLEDGE SYSTEM THAT ENHANCES SOURCE CONTENT BY IDENTIFYING CONTENT TOPICS AND KEYWORDS AND INTEGRATING ASSOCIATED/RELATED CONTENTS - A data structuring and artificial intelligence (AI), natural language processing (NLP) and Machine Learning knowledge system that enhances source content by identifying content topics and keywords and integrating associated and related internal and external content along with extracted information such as summaries, conclusions, action items, time sensitive topics, etc., is disclosed. The data structuring and AI/NLP/Machine Learning knowledge system includes an intelligent document viewer system and a communication sub-system with an objective communication system, an objective calendar communication system, and voice commands/responses system. | 10-15-2015 |
20150294226 | INFORMATION PROCESSING APPARATUS, INFORMATION PROCESSING METHOD AND PROGRAM - An information processing apparatus optimizes an action in a transition model in which a number of objects in each state transits according to the action. A cost constraint acquisition unit acquires multiple cost constraints including one that constrains a total cost of the action over at multiple timings and/or multiple states. A processing unit assumes action distribution in each state at each timing as a decision variable in an optimization problem and maximizes an objective function subtracting a term based on an error between an actual number of objects with the action in each state at each timing and an estimated number of objects in each state at each timing based on state transition by the transition model, from a total reward in a whole period, satisfying the multiple cost constraints. An output unit outputs the action distribution in each state at each timing that maximizes the objective function. | 10-15-2015 |
20150294233 | SYSTEMS AND METHODS FOR AUTOMATIC METADATA TAGGING AND CATALOGING OF OPTIMAL ACTIONABLE INTELLIGENCE - Disclosed herein are systems and methods for automatic metadata tagging and cataloging of optimal actionable intelligence. According to an aspect, a method for automatic metadata tagging and cataloging optimal actionable intelligence includes using at least one processor and memory for receiving at least one training attribute of a plurality of pre-identified attributes. The method further includes creating at least one metadata classifier tag based on the at least one training attribute. The method also includes receiving a data feed from at least one data feed source for analysis based on the at least one metadata classifier tags. The method also includes applying the created metadata classifier tags to the data feed. The method further includes detecting a sensor event based on the applied metadata classifier tag to the data feed. | 10-15-2015 |
20150296026 | SYSTEM AND METHOD FOR INTERACTION ROUTING BY APPLYING PREDICTIVE ANALYTICS AND MACHINE LEARNING TO WEB AND MOBILE APPLICATION CONTEXT - A system and method that pre-configure a telecommunication path between two users across a network. A server communicates an Application Programming Interface (API) to a website of an entity which, when accessed by a first user at a remote terminal, loads the API into the user terminal, captures data representative of the interaction of the first user with the website in real time, and communicates the data across the network to the server. The server compares the data with stored attributes of second users to identify a matching second user. The data is analyzed and used to predict a successful outcome between the two users. The server selects a network address of the matched second user, and, upon initiation of a telecommunication with the entity by the first user, routes the telecommunication to the network address and communicates the data to a second terminal of the matched second user. | 10-15-2015 |
20150297143 | ASSESSING PATIENT RISK FOR AN ACUTE HYPOTENSIVE EPISODE - What is disclosed is a system and method for assessing patient risk for an acute hypotensive episode. In one embodiment, the present method involves retrieving a training set from a database. The training set comprises mean arterial pressures (MAPs) for a plurality of subjects. Each MAP comprises systolic and diastolic measurements. The training set is used to train the present classifier system. Once trained, the present classifier system classifies an unclassified patient into either a first class or a second class. The first class is at risk for an acute hypotensive episode occurring within a prediction window of w≧60 minutes in the future. The second class is not at risk for an acute hypotensive episode. A MAP of an unclassified patient is retrieved or otherwise obtained. Thereafter, the present classifier system proceeds to classify the patient into the first or second class. Various embodiments are disclosed. | 10-22-2015 |
20150301724 | METHOD AND SYSTEM OF PROVIDING A PICTURE PASSWORD FOR RELATIVELY SMALLER DISPLAYS - Embodiments described herein relate to a device operable to process input for a picture password for proof of knowledge. In some embodiments, the device includes a display, an input subsystem, processor(s), and memory containing instructions executable by the processor(s) such that the device is operative to display, on the display of the device, an image for the picture password proof of knowledge. The image is associated with an overlaid grid comprising a plurality of elements, and each element corresponds to a distinct area of the image. The device is further operative to, determine an offset to be used and, in response to receiving an input via the input subsystem at a first location of the display, highlight an element of the overlaid grid at a second location on the first image on the display. The second location is offset from the first location by the offset. | 10-22-2015 |
20150302304 | CLOUD COMPUTING SCORING SYSTEMS AND METHODS - There is disclosed a computer-implemented cloud computing scoring system. In an embodiment, a parser receives unstructured sentiment data commenting on a scored service. The parser identifies in the unstructured sentiment data a service category of the scored service. The parser selects from the unstructured sentiment data text relating to the service category and matching one or more opinionative words and phrases listed in a keyword dictionary, thereby producing a structured comment associated with the service category. The structured comment is classified as positive or negative according to a list of exemplary sentiment data sets contained in a learning seed file. The exemplary sentiment data sets are manually assigned a positive or a negative polarity. The learning seed file is configured for enhancement by the ongoing addition of structured sentiment data, the structured sentiment data commenting on the scored service and having a polarity classification. | 10-22-2015 |
20150302310 | METHODS FOR DATA COLLECTION AND ANALYSIS FOR EVENT DETECTION - Behavior modeling includes how to detect and/or predict events based on observed changes in behavior. Detection of behavior that indicates possible adverse health events is performed by remote observation of a person's behavior. Captured data is correlated with an appropriate person, without identifying the person. People are associated with objects/locations, in the environment based on how the people relate to those objects/locations. Thus, people are identified based on their body characteristics or movement. Person specific data captured is labeled with unique identifiers. The location of certain objects/locations is correlated with the behavior profile to capture and analyze a nested pattern within a larger behavior pattern. Next to certain objects, certain types of behaviors/movements are expected. However, if the movement at a determined point in time deviates significantly from “normal” behavior patterns, such deviation may be an indication that something is wrong. | 10-22-2015 |
20150302312 | Predictive Modeling Based Focus Error Prediction - Predictive modeling based focus error prediction method and system are disclosed. The method includes obtaining wafer geometry measurements of a plurality of training wafers and grouping the plurality of training wafers to provide at least one training group based on relative homogeneity of wafer geometry measurements among the plurality of training wafers. For each particular training group of the at least one training group, a predictive model is develop utilizing non-linear predictive modeling. The predictive model establishes correlations between wafer geometry parameters and focus error measurements obtained for each wafer within that particular training group, and the predictive model can be utilized to provide focus error prediction for an incoming wafer belonging to that particular training group. | 10-22-2015 |
20150302316 | SYSTEM AND METHOD FOR DETERMINING UNWANTED PHONE MESSAGES - A computer-implemented method for generating a machine-learning model can include receiving, at a computing device having one or more processors, a plurality of reported phone numbers from telephone users, a plurality of posted phone numbers from one or more websites, and transcriptions of messages associated with a plurality of calling phone numbers. The machine-learning model is generated based on these various inputs and stored at the computing device. The model is configured to determine a probability that an unknown phone message is unwanted based on a phone number from which the unknown phone message originated. | 10-22-2015 |
20150302317 | NON-GREEDY MACHINE LEARNING FOR HIGH ACCURACY - Non-greedy machine learning for high accuracy is described, for example, where one or more random decision trees are trained for gesture recognition in order to control a computing-based device. In various examples, a random decision tree or directed acyclic graph (DAG) is grown using a greedy process and is then post-processed to recalculate, in a non-greedy process, leaf node parameters and split function parameters of internal nodes of the graph. In various examples the very large number of options to be assessed by the non-greedy process is reduced by using a constrained objective function. In examples the constrained objective function takes into account a binary code denoting decisions at split nodes of the tree or DAG. In examples, resulting trained decision trees are more compact and have improved generalization and accuracy. | 10-22-2015 |
20150302318 | UPDATING PREDICTION MODEL - In an approach to updating a prediction model, where the prediction model is used for time series data, a computer selects a first prediction time window in an order from a plurality of prediction time windows associated with the prediction model, and predicts one or more predicted values of the time series data at a plurality of time points within the first prediction time window. The computer calculates a prediction error associated with the first prediction time window based on the one or more predicted values and one or more actual measured values of the time series data at the plurality of time points. The computer determines whether the prediction error is larger than a predefined error threshold associated with the first prediction time window, and in response to determining the prediction error is larger than the predefined error threshold, provides a notification of updating the prediction model. | 10-22-2015 |
20150310139 | APPLICATION BEHAVIOR LEARNING BASED CAPACITY FORECAST MODEL - Various techniques employed by an application performance management service to generate an application behavior learning based capacity forecast model are disclosed. In some embodiments, such a capacity forecast model is at least in part generated by clustering collected transaction data into one or more usage patterns, analyzing collected usage pattern data, and solving a mathematical model generated from the usage pattern data to determine a sensitivity of a resource to each type of transaction associated with an application. | 10-29-2015 |
20150310162 | Compound Design Device, Compound Design Method, And Computer Program - When the interaction of a compound is predicted by using a computer, a technique to highly precisely design a compound having a novel structure has been required. A compound designing device is provided which includes an input unit configured to receive, at least about one or more query proteins, one or more pieces of query protein information corresponding to the one or more query proteins; and a processing unit configured to perform steps of (a) generating one or more pieces of compound information, (b) computing a score indicating interaction potential between a compound corresponding to the compound information and each of the one or more query proteins, (c) updating the compound information by an optimization method with reference to the score computed at step (b) such that the interaction potential increases, and (d) repeating steps (b) and (c) a plurality of times. | 10-29-2015 |
20150310330 | Computer-implemented method and system for digitizing decision-making processes - A computer-implemented method and system defines a uniform decision-tree formation to store decision-making processes. Each node in a decision tree represents a factor decision. All nodes of a decision tree are interlinked in a hierarchical structure based on a decision-making process. Any decision tree of the present invention can serve as a sub-tree of another decision tree. Users can convert their decision-making processes into decision trees and make collaborative decisions through network. | 10-29-2015 |
20150310332 | Predicting outcome based on input - A method, system and product for predicting an outcome of a program based on input. The method comprising: obtaining an input to be used by a program prior to executing the program; predicting by, a machine learning module, a predicted outcome of the program based on the input; wherein the predicted outcome is selected from the group consisting of: a pass outcome and a fail outcome, wherein the pass outcome is the program executing without failing when using the input, and wherein the fail outcome is the program failing when using the input. | 10-29-2015 |
20150310334 | METHOD AND APPARATUS FOR ASSESSING USER EXPERIENCE - A method of assessing user experience is disclosed. The method comprises the steps of monitoring network data and user-user equipment interaction data for a plurality of users within the network (step | 10-29-2015 |
20150310335 | DETERMINING A PERFORMANCE PREDICTION MODEL FOR A TARGET DATA ANALYTICS APPLICATION - A performance prediction model for a target data analytics application, where: (i) a reference data analytics application similar to the target data analytics application is determined; (ii) a configuration-performance data pair of the target data analytics application are acquired; and (iii) the performance prediction model for the target data analytics application is determined based on the configuration-performance data pair of the target data analytics application and a configuration-performance data pair of the at least one reference data analytics application. This can reduce the time required to accumulate the configuration-performance data pairs for determining the performance prediction model by combining the configuration-performance data pairs of the existing data analytics applications, thereby accelerating determination of the performance prediction model. | 10-29-2015 |
20150310336 | PREDICTING CUSTOMER CHURN IN A TELECOMMUNICATIONS NETWORK ENVIRONMENT - Embodiments of the present disclosure may provide a platform configured to forecast customer churn in a telecommunication network. The platform may be configured to receive customer activity data. The platform may then compute features associated with the customer activity data. These features are then inputted into a machine learning model used for predicting customer churn. Finally, the platform may then provide a report indicating customer churn predictions. The platform may be trained in a training phase prior to entering a prediction phase. | 10-29-2015 |
20150310346 | OPTIMIZATION DEVICE, OPTIMIZATION METHOD AND OPTIMIZATION PROGRAM - An optimization device includes: a selection unit 101 which selects a node to be played out in a solution search in an optimization calculation from among nodes as options in a search tree; a first calculation unit 102 which executes a playout from the selected node to search for a solution; and a second calculation unit 103 which sets the solution after the playout as an initial solution to search for a solution by a heuristic method, a local search method, or a neighborhood search method. | 10-29-2015 |
20150310351 | PROFILING A POPULATION OF EXAMPLES - A method for profiling a population of examples includes a computer receiving a dataset representative of the population of examples, a user selection of a population constraint, and an indication of a goal. The computer generates shallow fixed-depth trees based on the dataset and determines a collection of leaves of the shallow fixed-depth trees meeting the population constraint. Next, the computer sorts the collection of leaves based on a degree to which the goal is met. Then, the computer creates one or more profiles based on the collection of leaves. | 10-29-2015 |
20150310352 | SYSTEMS AND METHOD FOR PERFORMING CONTEXTUAL CLASSIFICATION USING SUPERVISED AND UNSUPERVISED TRAINING - Computerized systems and methods are disclosed for performing contextual classification of objects using supervised and unsupervised training. In accordance with one implementation, content reviewers may review training objects and submit supervised training data for preprocessing and analysis. The supervised training data may be preprocessed to identify key terms and phrases, such as by stemming, tokenization, or n-gram analysis, and form vectorized objects. The vectorized objects may be used to train one or more models for subsequent classification of objects. In certain implementations, preprocessing or training, among other steps, may be performed in parallel over multiple machines to improve efficiency. The disclosed systems and methods may be used in a wide variety of applications, such as article classification and content moderation. | 10-29-2015 |
20150317363 | LOAD SHEDDING IN A DATA STREAM MANAGEMENT SYSTEM - A data stream management system (DSMS) receives an input data stream from data stream sources and respective location information associated with sets of the data stream sources. A continuous query is executed against data items received via the input data streams to generate at least one client output data stream. A load shedding process is executed when the DSMS is overloaded with data from the input data streams. When the DSMS is not overloaded and for the location information associated with each of the data stream source sets, a respective utility value is determined indicating a utility to the client of data from the data stream source sets. The location information is stored in association with the corresponding data utility value. The location information received when the DSMS is overloaded is used, together with the data utility values, to identify input data streams whose data items are to be discarded. | 11-05-2015 |
20150317389 | Learning Multimedia Semantics from Large-Scale Unstructured Data - Systems and methods for learning topic models from unstructured data and applying the learned topic models to recognize semantics for new data items are described herein. In at least one embodiment, a corpus of multimedia data items associated with a set of labels may be processed to generate a refined corpus of multimedia data items associated with the set of labels. Such processing may include arranging the multimedia data items in clusters based on similarities of extracted multimedia features and generating intra-cluster and inter-cluster features. The intra-cluster and the inter-cluster features may be used for removing multimedia data items from the corpus to generate the refined corpus. The refined corpus may be used for training topic models for identifying labels. The resulting models may be stored and subsequently used for identifying semantics of a multimedia data item input by a user. | 11-05-2015 |
20150317442 | METHOD AND SYSTEM FOR ADVANCED ANEURYSM ANALYSIS - An automated method for aneurysm analysis including: extracting shape descriptors from test vessel data; generating an aneurysm probability map for the test vessel data using the shape descriptors; detecting the presence of the aneurysm on the test vessel data; localizing the aneurysm in the probability map; and separating the aneurysm from the probability map. | 11-05-2015 |
20150317444 | Identification of a Person Having Risk for Developing Type 2 Diabetes - The present invention relates to the identification of a person having risk for developing type 2 diabetes (T2D) by determining the presence or absence of specific genes, gene clusters, genera or species of bacteria in the person's gastrointestinal microbiota. More specifically the invention relates to a model to identify an individual having or at risk of developing type 2 diabetes (T2D) using metagenomic clusters (MGCs), wherein said model is characterised by using different metagenomic clusters for different population groups. Also provided is the use of such a model in the identification of a person having risk for developing type 2 diabetes (T2D). | 11-05-2015 |
20150317561 | Predicting and Enhancing Document Ingestion Time - A mechanism is provided in a data processing system for predicting and enhancing ingestion time for a set of input documents. The mechanism receives a set of documents to be added to a corpus of the data processing system. The mechanism records document features of each document within the set of documents using an annotation engine within the data processing system. The mechanism predicts an ingestion time for each document within the set of documents based on the document characteristics and a machine learning model. The mechanism assigns the set of documents to data processing system resources to be processed based on the predicted ingestion time for each document. | 11-05-2015 |
20150317563 | PREDICTING APPLICATION PERFORMANCE ON HARDWARE ACCELERATORS - Predicting program performance on hardware devices, in one aspect, may comprise obtaining a set of existing applications and observed performance on a target hardware device. The set of existing applications are run on one or more general purpose computer processors and application features are extracted from the existing application. A machine learning technique is employed to train a predictive model based on the extracted application features and the observed performance for predicting application performance on the target hardware device. | 11-05-2015 |
20150324065 | System and Method to Automatically Aggregate and Extract Key Concepts Within a Conversation by Semantically Identifying Key Topics - Methods are provided for providing a user with real-time access to supplemental information about named entities that appear within a conversation in a messaging application on a user terminal. Named entities, conversational topics, and sentiments are recognized as the user enters messages into the application. These are provided to a semantic search engine in a server system, that classifies the named entities into one of a variety of domains. Each domain has an associated tool for retrieving detailed supplemental information about the named entity. The server system transmits, to the user terminal, indicia that allow the user terminal to retrieve the supplemental information. Advertising related to a named entity having favorable sentiment also may be transmitted to the user terminal for display. These functions occur without the need for a separate search interface in the messaging application. | 11-12-2015 |
20150324459 | METHOD AND APPARATUS TO BUILD A COMMON CLASSIFICATION SYSTEM ACROSS MULTIPLE CONTENT ENTITIES - A content classification system classifies documents of a plurality of content entities into a hierarchical discipline structure. The content classification system receives a set of taxonomic labels collectively defining a hierarchical taxonomy and a plurality of documents. Each document is associated with one of the content entities. The content classification system extracts features from the received documents. A learned model is generated for assigning taxonomic labels to documents associated with a representative content entity using the features extracted from documents associated with the representative content entity. The content classification system assigns one or more taxonomic labels to each document of the other content entities using the learned model applied to the features extracted from the respective document. The documents of the plurality of content entities are classified based on the assigned taxonomic labels. | 11-12-2015 |
20150324693 | PREDICTING DRUG-DRUG INTERACTIONS BASED ON CLINICAL SIDE EFFECTS - A processor-implemented method, computer program product and system are provided for predicting drug-drug interactions based on clinical side effects. The method includes constructing a drug-drug interactions training dataset that includes pharmaceutical, pharmacokinetic or pharmacodynamics drug-drug interactions from multiple data sources for each of a plurality of drugs. The method also includes constructing side effect features for each of the drugs from side effects associated with the drugs. The method further includes building, using the drug-drug interactions training dataset, a drug-drug interactions classifier that predicts adverse drug-drug interactions for drug pairs derivable from the drugs. The method additionally includes for each of the side effects, building a two-by-two table using the side effect features, and performing a Fisher's exact test using the two-by-two table to determine whether a given one of side effects is differentially shown between positive predicted drug-drug interactions and negative predicted drug-drug interactions. | 11-12-2015 |
20150324694 | DISCOVERING USER-BEHAVIOR FROM TRAJECTORY-AS-POLYGON (TaP) - Disclosed is a system for analyzing user-behavior using a TaP algorithm. For example, raw data are collected and segmented to become segmented data. In this example, the TaP algorithm in combination with a sliding time window is implemented to derive a convex hull polygon. A determination of geometric properties of the derived convex hull polygon facilitates the analysis of the user-behavior. | 11-12-2015 |
20150324699 | NETWORK INFORMATION METHODS DEVICES AND SYSTEMS - Methods and systems for predicting links in a network, such as a social network, are disclosed. The existing network structure can be used to optimize link prediction. The methods and systems can learn a distance metric and/or a degree preference function that are structure preserving to predict links for new/existing nodes based on node properties. | 11-12-2015 |
20150324701 | APPARATUS FOR REPRESENTING SENSORY EFFECT AND METHOD THEREOF - Provided is a sensory effect representation apparatus including a collection unit configured to collect user status information about a user status, a storage unit configured to store sensory effects; an analysis unit configured to analyze the user status information to generate a user status prediction pattern, a sensory effect recommendation unit configured to recommend a sensory effect corresponding to the user status prediction pattern, from among the stored sensory effects; and a sensory effect provision unit configured to read a sensory effect corresponding to the sensory effect prediction pattern, from the storage unit and provide the read sensory effect. Accordingly, it is possible to provide a sensory effect appropriate for a user characteristic by predicting a user status and reflecting a feedback on a recommended sensory effect. | 11-12-2015 |
20150326680 | SYSTEM AND METHOD FOR ESTIMATING INTEREST IN, ACTIVITY AT AND OCCUPANCY OF A PHYSICAL LOCATION - Techniques for determining levels of interest, activity, or occupancy at a physical location can include receiving data corresponding to physical parameters sensed by a plurality of sensors at the physical location. The physical parameters can include temperature, humidity, pressure, sound, distance to an object, visible light, infra-red light, motion of objects, acceleration, magnetic field, vibration, and radio signals. Synthetic variables can be generated based on the received data and can represent a processed or combined value for its corresponding physical parameters. The physical parameters and synthetic variables can be stored in a memory device. One or more indicators for a level of: (i) interest, (ii) activity, or (iii) occupancy at the physical location can be generated based on the received data and the one or more synthetic variables by utilizing a machine learning model and output to a user computing device for display in a user interface. | 11-12-2015 |
20150331993 | Custom Knowledgebases and Sequence Datasets - Illustrative embodiments of custom knowledgebases and sequence datasets, as well as related methods, are disclosed. In one illustrative embodiment, one or more computer-readable media may comprise a custom knowledgebase and an associated sequence dataset. The custom knowledgebase may comprise a plurality of assertions that have been automatically extracted from a plurality of publications, where each of the plurality of assertions encodes a relationship between a subject and an object. The sequence dataset may comprise a plurality of called biological sequences, where each of the plurality of called biological sequences is associated with one or more of the plurality of assertions of the custom knowledgebase. | 11-19-2015 |
20150332145 | TRAFFIC SHAPING BASED ON PREDICTED NETWORK RESOURCES - In one embodiment, a committed information rate (CR) prediction is received from a machine learning model that corresponds to a predicted average traffic rate supported by a network connection. A traffic shaping strategy is adjusted based on the CR prediction. A rate at which data is communicated over the network connection may be based on the traffic shaping policy. The effects of the adjusted traffic shaping strategy are also monitored. Feedback is further provided to the machine learning model based on the monitored effects of the adjusted traffic shaping strategy. | 11-19-2015 |
20150332147 | Technique For Determining The Root Cause Of Web Site Performance Or Availability Problems - Automated techniques are provided for determining root causes of web site performance or availability problems. Performance metrics falling within a data analysis window are evaluated by a performance monitoring tool, where the performance metrics pertain to the loading of a web page. From the data analysis, particular problems may be surfaced for further consideration. Root causes are also determined for the surfaced problems and published by the performance monitoring tool. | 11-19-2015 |
20150332153 | METHOD FOR ANALYZING GEOGRAPHICAL REGIONS AND DETECTING AREAS OF INTEREST - A method for analyzing geographical regions and detecting areas of interest by a exploration system comprising a sensor and a digital data processing center. The method includes a first phase of learning the intrinsic appearance of each geographical region, in which a database of knowledge comprising groups of digital data is generated. The method further includes a second phase of detecting areas of interest in the geographical regions. The phases includes acquiring a piece of digital data corresponding to a geographical region by the sensor, transmitting the acquired digital data to the digital data processing center, and processing the digital data by the digital data processing center. | 11-19-2015 |
20150332155 | PREDICTIVE PATH CHARACTERISTICS BASED ON NON-GREEDY PROBING - In one embodiment, a network device receives metrics regarding a path in the network. A predictive model is generated using the received metrics and is operable to predict available bandwidth along the path for a particular type of traffic. A determination is made as to whether a confidence score for the predictive model is below a confidence threshold associated with the particular type of traffic. The device obtains additional data regarding the path based on a determination that the confidence score is below the confidence threshold. The predictive model is updated using the additional data regarding the path. | 11-19-2015 |
20150332165 | Hierarchical hybrid batch-incremental learning - In one embodiment, a machine learning model for predicting one or more metrics is run in a network which includes a centralized controller device interconnected with a plurality of edge devices. A batch version of the machine learning model that operates in batch mode is hosted at the centralized controller device. Then, an incremental version of the machine learning model that operates in incremental mode is pushed to an edge device of the plurality of edge devices, such that the incremental version of the machine learning model is hosted at the edge device. As a result, the batch version and the incremental version of the machine learning model run in parallel with one another. | 11-19-2015 |
20150332167 | SYSTEM AND METHOD FOR MODELING AND/OR ANALYZING MANUFACTURING PROCESSES - Systems and techniques for modeling and/or analyzing manufacturing processes are presented. A dataset component generates a plurality of binary classification datasets based on process data associated with one or more fabrication tools. A learning component generates a plurality of learned models based on the plurality of binary classification datasets and applies a weight to the plurality of learned models based on a number of data samples associated with the plurality of binary classification datasets to generate a weighted plurality of learned models. A merging component merges the weighted plurality of learned models to generate a process model for the process data. | 11-19-2015 |
20150332168 | DETECTION OF COMMUNICATION TOPIC CHANGE - A computer processor determines a first span of a communication, wherein a span includes content associated with one or more dialog statements. If the content of the first span contains one or more topic change indicators which are identified by at least one detector of a learning model, the computer processor, in response, generates scores for each of the one or more indicators. The computer processor aggregates scores of the one or more indicators of the first span, which may be weighted, to produce an aggregate score. The computer processor compares the aggregate score to a threshold value, wherein the threshold value is determined during training of the learning model, and the computer processor, in response to the aggregate score crossing the threshold value, determines a topic change has occurred within the first span. | 11-19-2015 |
20150332169 | INTRODUCING USER TRUSTWORTHINESS IN IMPLICIT FEEDBACK BASED SEARCH RESULT RANKING - User trustworthiness may be introduced in implicit feedback based supervised machine learning systems. A set of training data examples may be scored based on the trustworthiness of users associated respectively with the training data examples. The training data examples may be sampled into a plurality of training data sets based on a weighted bootstrap sampling technique, where each weight is a probability proportional to trustworthiness score associated with an example. A machine learning algorithm takes the plurality of the training data sets as input and generates a plurality of trained models. Outputs from the plurality of trained models may be ensembled by computing a weighted average of the outputs of the plurality of trained models. | 11-19-2015 |
20150332170 | ELECTRONIC SYSTEM WITH LOW CONFIDENCE PREDICTION AND METHOD OF OPERATION THEREOF - An electronic system includes: a base circuit configured to determine a weight for a base prediction; an alternate circuit, coupled to the base circuit, configured to calculate a bit combined with the weight for an alternate prediction; and electronic circuits, coupled to the alternate circuit, configured to select the base prediction or the alternate prediction based on a threshold. | 11-19-2015 |
20150332172 | LEARNING METHOD, INFORMATION PROCESSING DEVICE, AND RECORDING MEDIUM - A learning method includes: randomly selecting one or more feature vectors from feature vectors for learning to form a sample set, by a processor; selecting, from the feature vectors for learning, one of feature vectors appended with a label different from a label appended to a feature vector included in the sample set as a reference vector, the selecting being carried out based on a generalized average of distance from a feature vector included in the sample set, by the processor; and learning a hyperplane that divides a feature vector space, the learning being carried out using a pair of one of feature vectors appended with a label different from a label appended to the reference vector, among the feature vectors for learning, and the selected reference vector, by the processor. | 11-19-2015 |
20150332173 | LEARNING METHOD, INFORMATION CONVERSION DEVICE, AND RECORDING MEDIUM - A learning method includes: counting any one of or some of the number of labels added to each of feature amount vectors included in a learning data set, the number of types of the label, the number of feature amount vectors added with the same label, and the number of data pairs used for learning of a hyperplane, by a processor; first selecting, according to a result of the counting, one or more generation methods from a plurality of previously stored generation methods that generate the data pairs from the learning data set, by the processor; generating, using the selected generation methods, the data pairs from the feature amount vectors included in the learning data set, by the processor; and first learning, using the generated data pairs, the hyperplane that divides a feature amount vector space, by the processor. | 11-19-2015 |
20150339573 | Self-Referential Semantic-based Method, System, and Device - A self-referential semantic-based method, system, and device links semantic chains comprising subject, predicate, and objects of the predicate and optionally, an associated probability, with behavioral-based chains comprising a computer-implemented system, predicate, and an object of the predicate, and generates self-referential communications based on the linked chains. The self-referential communications may include metaphorical and/or imaginative constructs, and may convey a sense of confidence or what has been learned over time in accordance with weightings associated with the semantic and/or behavioral chains. The self-referential semantic-based device may take the form of a self-propelled apparatus, including an apparatus in humanoid form. | 11-26-2015 |
20150339577 | Generating a Classifier for Performing a Query to a Given Knowledge Base - A computer device for generating a classifier for performing a query to a given knowledge base is provided. The given knowledge base includes predicates, subjects and objects related to each other. The computer device includes a selection entity for selecting one of the predicates, and a triple generation entity for generating, based on the given knowledge base, triples. Each of the triples includes the one selected predicate, and a subject and an object related to the one selected predicate. The computer device also includes a candidate generation entity for generating a list of properties. Each property of the list of properties is correlated to the subject and the object of one of the triples by performing a context-based query within the given knowledge base. The computer device includes a classifier generation entity for generating a classifier having the list of properties related to the selected predicate. | 11-26-2015 |
20150339583 | MACHINE LEARNING AND VALIDATION OF ACCOUNT NAMES, ADDRESSES, AND/OR IDENTIFIERS - Systems and methods are disclosed for determining if an account identifier is computer-generated. One method includes receiving the account identifier, dividing the account identifier into a plurality of fragments, and determining one or more features of at least one of the fragments. The method further includes determining the commonness of at least one of the fragments, and determining if the account identifier is computer-generated based on the features of at least one of the fragments, and the commonness of at least one of the fragments. | 11-26-2015 |
20150339585 | Method for Measuring Individual Entities' Infectivity and Susceptibility in Contagion - Measurements of individual-level infectivity, susceptibility and baseline infection risk to biological or social contagion are made for large number of entities from their contact relation, sequential infection occurrences and environmental data, using computer implemented MCMC for a Bayesian estimation of an integrative latent trait response model. The method is useful for precise and efficient contagion control and prevention. | 11-26-2015 |
20150339589 | APPARATUS AND METHODS FOR TRAINING ROBOTS UTILIZING GAZE-BASED SALIENCY MAPS - Robotic devices may be trained using saliency maps derived from gaze of a trainer. In navigation applications, the saliency map may correspond to portions of the environment being observed by a driving instructor during training using a gaze detector. During an operation, a driver assist robot may utilize the saliency map in order to assess attention of the driver, detect potential hazards, and issue alerts. Responsive to a detection of a mismatch between the driver current attention and the target attention derived from the saliency map, the robot may issue a warning, and/or prompt the driver of an upcoming hazard. A data processing apparatus may employ gaze based saliency maps in order to analyze, e.g., surveillance camera feeds for intruders, open doors, hazards, policy violations (e.g., open doors). | 11-26-2015 |
20150339590 | SYNTHETIC QUESTION FORMULATION - Briefly, embodiments disclosed herein may relate to formulating synthetic questions, such as in response to a search query, for example. Candidate synthetic questions may be presented to a user who may initiate a search at least in part by selecting one or more candidate synthetic questions, for example, in an embodiment. | 11-26-2015 |
20150339591 | Collegial Activity Learning Between Heterogeneous Sensors - Unlabeled and labeled sensor data is received from one or more source views. Unlabeled, and optionally labeled, sensor data is received from a target view. The received sensor data is used to train activity recognition classifiers for each of the source views and the target view. The sources and the target each include one or more sensors, which may vary in modality from one source or target to another source or target. | 11-26-2015 |
20150339592 | SITE FLOW OPTIMIZATION - In an example embodiment, for each of a plurality of possible presented actions corresponding to a particular flow of actions in a computer network, and for each of one or more possible performed actions for each possible presented action, a likelihood that a user will perform the possible performed action is determined. Then, a first presented action is identified by determining a presented action having a maximum determined likelihood, based on the determined likelihood, wherein the identifying a first presented action includes utilizing a machine learning model having one or more user covariates and one or more performed action covariates, and interactions between the one or more user covariates and the one or more performed action covariates, the user covariates including information specific to the user, the one or more performed action covariates including information specific to one or more of the possible performed actions. | 11-26-2015 |
20150347905 | MODELING USER ATTITUDES TOWARD A TARGET FROM SOCIAL MEDIA - Embodiments relate to user attitude modeling and behavior prediction for a social media network. One aspect includes collecting data relating to previously demonstrated sentiments, opinions, and actions attributed to network users toward a topic. Another aspect includes creating a model from the data, which includes factorizing the actions for behavior inference, factorizing auxiliary content from the network for opinion and sentiment inferences, and applying sentiment and opinion regularization to constrain user preferences on implicit topics to explicit sentiments and explicit opinions. Another aspect includes applying the model to a new user of the network with respect to the topic, and generating a prediction with respect to the user that includes predicting sentiment and opinion as a function of the auxiliary content and feature coefficients learned during a training process, and predicting a future action of the user as a function of the auxiliary content and latent profiles of the topic. | 12-03-2015 |
20150347907 | METHODS AND SYSTEM FOR MANAGING PREDICTIVE MODELS - Disclosed herein is a technique for implementing a framework that enables application developers to enhance their applications with dynamic adjustment capabilities. Specifically, the framework, when utilized by an application on a mobile computing device that implements the framework, can enable the application to establish predictive models that can be used to identify meaningful behavioral patterns of an individual who uses the application. In turn, the predictive models can be used to preempt the individual's actions and provide an enhanced overall user experience. The framework is configured to interface with other software entities on the mobile computing device that conduct various analyses to identify appropriate times for the application to manage and update its predictive models. Such appropriate times can include, for example, identified periods of time where the individual is not operating the mobile computing device, as well as recognized conditions where power consumption is not a concern. | 12-03-2015 |
20150347908 | METHODS AND SYSTEM FOR MANAGING PREDICTIVE MODELS - Disclosed herein is a technique for implementing a framework that enables application developers to enhance their applications with dynamic adjustment capabilities. Specifically, the framework, when utilized by an application on a mobile computing device that implements the framework, can enable the application to establish predictive models that can be used to identify meaningful behavioral patterns of an individual who uses the application. In turn, the predictive models can be used to preempt the individual's actions and provide an enhanced overall user experience. The framework is configured to interface with other software entities on the mobile computing device that conduct various analyses to identify appropriate times for the application to manage and update its predictive models. Such appropriate times can include, for example, identified periods of time where the individual is not operating the mobile computing device, as well as recognized conditions where power consumption is not a concern. | 12-03-2015 |
20150347915 | METHOD AND APPARATUS FOR IDENTIFYING STRUCTURAL DEFORMATION - A method and apparatus for identifying deformation of a structure. Training deformation data is identified for each training case in a plurality of training cases. Training strain data is identified for each training case in the plurality of training cases. The training deformation data and the training strain data are configured for use by a heuristic model to increase an accuracy of output data generated by the heuristic model. A group of parameters for the heuristic model is adjusted using the training deformation data and the training strain data for the each training case in the plurality of training cases such that the heuristic model is trained to generate estimated deformation data for the structure based on input strain data. The estimated deformation data has a desired level of accuracy. | 12-03-2015 |
20150347918 | FUTURE EVENT PREDICTION USING AUGMENTED CONDITIONAL RANDOM FIELD - Systems and methods are disclosed for a future event prediction. Embodiments include capturing spatiotemporal data pertaining to activities, wherein the activities include a plurality of events, and employing an augmented-hidden-conditional-random-field (a-HCRF) predictor to generate a future event prediction based on a parameter-vector input, hidden states, and the spatiotemporal data. Methods therein utilize a graph including a first node associated with random variables corresponding to a future event state, a second node associated with random variables corresponding to spatiotemporal input data, a first group of nodes, each node therein associated with random variables corresponding to a subset of the spatiotemporal input data, a second group of nodes, each node therein associated with random variables corresponding to a hidden-state; wherein the edges connect the first node with the second node, the first node with the second group of nodes, and the first group of nodes with the second group of nodes. | 12-03-2015 |
20150347920 | SEARCH SYSTEM AND CORRESPONDING METHOD - There is provided a search system comprising a statistical model trained on text associated with a piece of content. The text associated with the piece of content is drawn from a plurality of different data sources. The system is configured to receive text input and generate using the statistical model an estimate of the likelihood that the piece of content is relevant given the text input. A corresponding method is also provided. | 12-03-2015 |
20150347922 | MULTI-MODEL BLENDING - A method and a system to perform multi-model blending are described. The method includes obtaining one or more sets of predictions of historical conditions, the historical conditions corresponding with a time T that is historical in reference to current time, and the one or more sets of predictions of the historical conditions being output by one or more models. The method also includes obtaining actual historical conditions, the actual historical conditions being measured conditions at the time T, assembling a training data set including designating the two or more set of predictions of historical conditions as predictor variables and the actual historical conditions as response variables, and training a machine learning algorithm based on the training data set. The method further includes obtaining a blended model based on the machine learning algorithm. | 12-03-2015 |
20150347923 | ERROR CLASSIFICATION IN A COMPUTING SYSTEM - In an approach to determining a classification of an error in a computing system, a computer receives a notification of an error during a test within a computing system. The computer then retrieves a plurality of log files created during the test from within the computing system and determines data containing one or more error categorizations. The computer determines a classification of the error, based, at least in part, on the plurality of log files and the data containing one or more error categorizations. | 12-03-2015 |
20150347924 | EMAIL OPTIMIZATION FOR PREDICTED RECIPIENT BEHAVIOR: DETERMINING A LIKELIHOOD THAT A PARTICULAR RECEIVER-SIDE BEHAVIOR WILL OCCUR - Techniques are described herein for predicting one or more behaviors by an email recipient and, more specifically, to machine learning techniques for predicting one or more behaviors of an email recipient, changing one or more components in the email to increase the likelihood of a behavior, and determining and/or scheduling an optimal time to send the email. Some advantages of the embodiments disclosed herein may include, without limitation, the ability to predict the behavior of the email recipient and suggest the characteristics of an email which will increase the likelihood of a positive behavior, such as a reading or responding to the email, visiting a website, calling a sales representative, or opening an email attachment. | 12-03-2015 |
20150347925 | EMAIL OPTIMIZATION FOR PREDICTED RECIPIENT BEHAVIOR: SUGGESTING CHANGES THAT ARE MORE LIKELY TO CAUSE A TARGET BEHAVIOR TO OCCUR - Techniques are described herein for predicting one or more behaviors by an email recipient and, more specifically, to machine learning techniques for predicting one or more behaviors of an email recipient, changing one or more components in the email to increase the likelihood of a behavior, and determining and/or scheduling an optimal time to send the email. Some advantages of the embodiments disclosed herein may include, without limitation, the ability to predict the behavior of the email recipient and suggest the characteristics of an email which will increase the likelihood of a positive behavior, such as a reading or responding to the email, visiting a website, calling a sales representative, or opening an email attachment. | 12-03-2015 |
20150347926 | Fast Naive Bayesian Framework with Active-Feature Ordering - The technology described uses a Naïve Bayes Classifier with Active-Feature Ordering to identify contributors to a contact database who are likely to be able to update an arbitrary contact. The technology disclosed further relates to identifying the n most likely records with a number of features, with each feature having a specific finite number of different possible values. The disclosed technology also describes using a Naïve Bayes Classifier with Active-Feature Ordering for diagnostic screening, to evaluate a patient's symptoms against a compendium of diseases to choose the diseases with the greatest posterior likelihood given the vector of observed symptoms of the patient. The disclosed technology additionally describes using a Naïve Bayes Classifier with Active-Feature Ordering for crowd sourcing tasks, using a sample data set that includes thousands of workers, to identify a worker, who is experienced, to complete a featured task. | 12-03-2015 |
20150347927 | CANONICAL CO-CLUSTERING ANALYSIS - A method and system are provided. The method includes determining from a data matrix having rows and columns, a clustering vector of the rows and a clustering vector of the columns. Each row in the clustering vector of the rows is a row instance and each row in the clustering vector of the columns is a column instance. The method further includes performing correlation of the row and column instances. The method also includes building a normalizing graph using a graph-based manifold regularization that enforces a smooth target function which, in turn, assigns a value on each node of the normalizing graph to obtain a Lapacian matrix. The method additionally includes performing Eigenvalue decomposition on the Lapacian matrix to obtain Eigenvectors. The method further includes providing a canonical co-clustering analysis function by maximizing a coupling between clustering vectors while concurrently enforcing regularization on each clustering vector using the Eigenvectors. | 12-03-2015 |
20150348127 | EMAIL OPTIMIZATION FOR PREDICTED RECIPIENT BEHAVIOR: SUGGESTING CHANGES IN AN EMAIL TO INCREASE THE LIKELIHOOD OF AN OUTCOME - Techniques are described herein for predicting one or more behaviors by an email recipient and, more specifically, to machine learning techniques for predicting one or more behaviors of an email recipient, changing one or more components in the email to increase the likelihood of a behavior, and determining and/or scheduling an optimal time to send the email. Some advantages of the embodiments disclosed herein may include, without limitation, the ability to predict the behavior of the email recipient and suggest the characteristics of an email which will increase the likelihood of a positive behavior, such as a reading or responding to the email, visiting a website, calling a sales representative, or opening an email attachment. | 12-03-2015 |
20150348128 | EMAIL OPTIMIZATION FOR PREDICTED RECIPIENT BEHAVIOR: SUGGESTING A TIME AT WHICH A USER SHOULD SEND AN EMAIL - Techniques are described herein for predicting one or more behaviors by an email recipient and, more specifically, to machine learning techniques for predicting one or more behaviors of an email recipient, changing one or more components in the email to increase the likelihood of a behavior, and determining and/or scheduling an optimal time to send the email. Some advantages of the embodiments disclosed herein may include, without limitation, the ability to predict the behavior of the email recipient and suggest the characteristics of an email which will increase the likelihood of a positive behavior, such as a reading or responding to the email, visiting a website, calling a sales representative, or opening an email attachment. | 12-03-2015 |
20150354336 | CONTROL PARAMETER DETERMINATION IN STEAM ASSISTED GRAVITY DRAINAGE OIL DRILLING - Drilling data including a plurality of values measured for each of a plurality of drilling parameters is received. An objective function that maximizes production of a material produced by a production operation, minimizes water usage by the production operation, and minimizes a sub-cool temperature of the material is determined using the received drilling data. An association rule that defines a range of values for a control parameter of the plurality of drilling parameters that is selected based on a value of a first parameter of the plurality of drilling parameters is determined using the received drilling data. Measured drilling data that indicates current control parameter values of the production operation is received. An optimal value for the control parameter is determined by executing the determined objective function with the received, measured drilling data as an input and subject to the determined association rule. The determined optimal value is output. | 12-10-2015 |
20150356403 | SYNTHETIC LOGGING FOR RESERVOIR STIMULATION - The present disclosure generally relates to synthetic logging for reservoir stimulation. A synthetic logging method for stimulating a reservoir includes: training a machine learning algorithm using historical or exploratory data; and generating a synthetic elastic property log of the reservoir by supplying the trained machine learning algorithm with data acquired from a production wellbore. | 12-10-2015 |
20150356404 | Method for Cognitive Information Processing - A cognitive information processing system environment which includes a plurality of data sources; a cognitive inference and learning system coupled to receive a data from the plurality of data sources, the cognitive inference and learning system processing the data from the plurality of data sources to provide cognitively processed insights, the cognitive inference and learning system further comprising performing a learning operation to iteratively improve the cognitively processed insights over time; and, a destination, the destination receiving the cognitively processed insights. | 12-10-2015 |
20150356405 | Insight Engine for Use Within a Cognitive Environment - An apparatus for use within a cognitive information processing system comprising: an insight/learning engine, the insight/learning engine encapsulating an operation, the operation being applied to a target cognitive graph to generate a cognitive insight. | 12-10-2015 |
20150356420 | Rating Difficulty of Questions - A mechanism is provided in a data processing system for rating difficulty of a question. The mechanism receives an input question and generates one or more candidate answers from a corpus of knowledge using a pipeline of software engines. The pipeline of software engines generates a plurality of features extracted from the question, the one or more candidate answers, or the corpus of knowledge. The mechanism then generates a question difficulty score based on the plurality of features using a machine learning model. The machine learning model maps features to assigned weights for scaling the difficulty score. | 12-10-2015 |
20150356421 | Method for Learning Exemplars for Anomaly Detection - A method detects anomalies in time series data, by first learning a final set of exemplars by summarizing training time series data using a divide-and-conquer procedure. Then, for each window of testing time series data, a distance to a nearest exemplar in the final set of exemplars is determined, wherein the distance is an anomaly score. Finally, an anomaly is signaled when the anomaly score for a window is greater than a threshold. | 12-10-2015 |
20150356422 | Cognitive Information Processing System Environment - A cognitive information processing system environment which includes a plurality of data sources; a cognitive inference and learning system coupled to receive a data from the plurality of data sources, the cognitive inference and learning system processing the data from the plurality of data sources to provide cognitively processed insights, the cognitive inference and learning system further comprising performing a learning operation to iteratively improve the cognitively processed insights over time; and, a destination, the destination receiving the cognitively processed insights. | 12-10-2015 |
20150356425 | Dataset Engine for Use Within a Cognitive Environment - An apparatus for use within a cognitive information processing system environment comprising: a dataset engine, the dataset engine coupled to receive data from a plurality of data sources, the dataset engine processing the data from the plurality of data sources to establish and maintain a dynamic data ingestion and enrichment pipeline. | 12-10-2015 |
20150356428 | Cognitive Interfaces for Use Within a Cognitive Environment - A cognitive information processing system comprising: a cognitive inference and learning system coupled to receive data from a plurality of data sources and to provide insights to a destination, the cognitive inference and learning system comprising a first interface, the first interface providing the data from the plurality of data sources to the cognitive interface and learning system, and, the cognitive inference and learning system comprising a second interface, the second interface providing the cognitively processed insights to the destination. | 12-10-2015 |
20150356429 | Method for Interfacing with a Cognitive Inference and Learning System - A method for interfacing with a cognitive inference and learning system comprising: processing data from a plurality of data sources to provide cognitively processed insights via a cognitive inference and learning system, the cognitive inference and learning system further comprising performing a learning operation to iteratively improve the cognitively processed insights over time; receiving the data from the plurality of data sources to the cognitive interface and learning system via a first interface, and, providing the cognitively processed insights to a destination via a second interface. | 12-10-2015 |
20150356430 | Cognitive Agents for Use Within a Cognitive Environment - An apparatus for providing cognitive insights comprising: a cognitive inference and learning system, the cognitive inference and learning system comprising a plurality of agents, the plurality of agents processing streams of data from a plurality of data sources, the processing the streams of data from the plurality of data sources via the plurality of agents performing a respective plurality of cognitive operations on the streams of data, at least one of the plurality of agents generating cognitive insights based upon the performing the respective plurality of cognitive operations on the streams of data from the plurality of data sources. | 12-10-2015 |
20150356431 | Method for Providing Cognitive Insights Using Cognitive Agents - A computer-implementable method for providing cognitive insights comprising: receiving streams of data from a plurality of data sources; processing streams of data from a plurality of data sources via a plurality of agents, the processing the streams of data from the plurality of data sources via the plurality of agents performing a respective plurality of cognitive operations on the streams of data; and, providing cognitive insights based upon the performing the respective plurality of cognitive operations on the streams of data from the plurality of data sources. | 12-10-2015 |
20150356432 | Hybrid Data Architecture for Use Within a Cognitive Environment - A data architecture for use within a cognitive information processing system environment comprising: a plurality of data sources, the plurality of data sources comprising a public data source and a private data source; and, a cognitive data management module, the cognitive data management module accessing information from the plurality of data sources and providing the information to an inference and learning system. | 12-10-2015 |
20150356433 | Method for Using Hybrid Data Architecture Within a Cognitive Environment - A method for receiving a plurality of types of data within a cognitive information processing system environment comprising: receiving data from a plurality of data sources, the plurality of data sources comprising a public data source and a private data source; accessing information from the plurality of data sources via a cognitive data management module; and, providing the information to an inference and learning system. | 12-10-2015 |
20150356434 | Travel Industry Optimized Cognitive Information Processing System Environment - A cognitive information processing system environment comprising: a plurality of data sources, at least some of the plurality of data sources comprising travel relevant data sources; a cognitive inference and learning system coupled to receive a data from the plurality of data sources, the cognitive inference and learning system processing the data from the plurality of data sources to provide cognitively processed travel relevant insights, the cognitive inference and learning system further comprising performing a learning operation to iteratively improve the cognitively processed travel relevant insights over time; and, a destination, the destination receiving the cognitively processed travel relevant insights. | 12-10-2015 |
20150356435 | Hybrid Data Architecture for Use Within a Travel Industry Optimized Cognitive Environment - A data architecture for use within a cognitive information processing system environment comprising: a plurality of data sources, the plurality of data sources comprising a public data source and a private data source, the public data source comprising publicly available travel information, the private data source comprising privately managed, company specific travel information; and, a cognitive data management module, the cognitive data management module accessing information from the plurality of data sources and providing the information to an inference and learning system. | 12-10-2015 |
20150356436 | Method for Using Hybrid Data Within a Travel Industry Optimized Cognitive Environment - A method for using hybrid data within a cognitive information processing system environment comprising: receiving data from a plurality of data sources, the plurality of data sources comprising a public data source and a private data source, the public data source comprising publicly available travel information, the private data source comprising privately managed, company specific travel information; accessing information from the plurality of data sources via a cognitive data management module; and, providing the information to an inference and learning system. | 12-10-2015 |
20150356438 | Travel-Related Cognitive Personas - A method, system and computer-usable medium for performing cognitive computing operations comprising receiving streams of data from a plurality of data sources; processing the streams of data from the plurality of data sources, the processing the streams of data from the plurality of data sources performing data enriching for incorporation into a cognitive graph; defining a travel-related cognitive persona within the cognitive graph, the travel-related cognitive persona corresponding to an archetype user model, the travel-related cognitive persona comprising a set of nodes in the cognitive graph; associating a user with the travel-related cognitive persona; and, performing a cognitive computing operation based upon the travel-related cognitive persona associated with the user. | 12-10-2015 |
20150356450 | Real-Time Risk Prediction During Drilling Operations - Systems and methods for real-time risk prediction during drilling operations using real-time data from an uncompleted well, a trained coarse layer model and a trained fine layer model for each respective layer of the trained coarse layer model. In addition to using the systems and methods for real-time risk prediction, the systems and methods may also be used to monitor other uncompleted wells and to perform a statistical analysis of the duration of each risk level for the monitored well. | 12-10-2015 |
20150356455 | SYSTEMS AND METHODS ASSOCIATED WITH AN AUTO-TUNING SUPPORT VECTOR MACHINE - Some embodiments are associated with a support vector machine having model parameters. According to some embodiments, a set of evaluation data may be received and a computer processor may automatically tune the model parameters during a training process using the set of evaluation data. The automatically tuned model parameters for the support vector machine may then be output directly from the training process. | 12-10-2015 |
20150356456 | Real-Time or Frequent Ingestion by Running Pipeline in Order of Effectiveness - A mechanism is provided in a data processing system for partial ingestion of content. The mechanism receives new content to be ingested into a corpus of information. The mechanism applies a plurality of sub-pipelines of annotation engines against the new content in order of effectiveness. The plurality of sub-pipelines include all annotation engines of an ingestion pipeline. Each sub-pipeline within the plurality of sub-pipelines generates one or more intermediate output objects. The mechanism provides access to the one or more intermediate output objects. | 12-10-2015 |
20150356457 | LABELING OF DATA FOR MACHINE LEARNING - A computer generates labels for machine learning algorithms by retrieving, from a data storage circuit, multiple label sets that contain labels that each classify data points in a corpus of data. A graph is generated that includes a plurality of edges, each edge between two respective labels from different label sets of the multiple label sets. Weights are determined for the plurality of edges based upon a consistency between data points classified by two labels connected by the edges. An algorithm is applied that groups labels from the multiple label sets based upon the weights for the plurality of edges. Data points are identified from the corpus of data that represent conflicts within the grouped labels. An electronic message is transmitted in order to present the identified data points to entities for further classification. A new label set is generated using the further classification received from the entities. | 12-10-2015 |
20150356458 | Method And System For Forecasting Future Events - Embodiments of the present invention provide a method comprising: providing training data for training at least one mathematical model, wherein the training data is based on past flight information of a plurality of passengers, and the training data comprises a first set of vectors and an associated target variable for each passenger in the plurality of passengers; training at least one mathematical model with the training data; and providing a second set of vectors relating to past flight information of the passenger as inputs to the trained at least one mathematical model and calculating an output of the trained at least one mathematical model based on the inputs, wherein the output represents a prediction of future flight activities of the passenger. | 12-10-2015 |
20150356459 | LABELING OF DATA FOR MACHINE LEARNING - A computer generates labels for machine learning algorithms by retrieving, from a data storage circuit, multiple label sets that contain labels that each classify data points in a corpus of data. A graph is generated that includes a plurality of edges, each edge between two respective labels from different label sets of the multiple label sets. Weights are determined for the plurality of edges based upon a consistency between data points classified by two labels connected by the edges. An algorithm is applied that groups labels from the multiple label sets based upon the weights for the plurality of edges. Data points are identified from the corpus of data that represent conflicts within the grouped labels. An electronic message is transmitted in order to present the identified data points to entities for further classification. A new label set is generated using the further classification received from the entities. | 12-10-2015 |
20150356460 | Method for Providing Travel Industry Optimized Cognitive Insights - A method for providing travel optimized cognitive insights comprising: receiving data from a plurality of data sources, at least some of the plurality of data sources comprising travel relevant data sources; processing the data from the plurality of data sources to provide cognitively processed insights; performing a learning operation to iteratively improve the cognitively processed insights over time; and, providing the cognitively processed travel relevant insights to a destination. | 12-10-2015 |
20150356461 | TRAINING DISTILLED MACHINE LEARNING MODELS - Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a distilled machine learning model. One of the methods includes training a cumbersome machine learning model, wherein the cumbersome machine learning model is configured to receive an input and generate a respective score for each of a plurality of classes; and training a distilled machine learning model on a plurality of training inputs, wherein the distilled machine learning model is also configured to receive inputs and generate scores for the plurality of classes, comprising: processing each training input using the cumbersome machine learning model to generate a cumbersome target soft output for the training input; and training the distilled machine learning model to, for each of the training inputs, generate a soft output that matches the cumbersome target soft output for the training input. | 12-10-2015 |
20150356462 | Using Normalized Confidence Values For Classifying Mobile Device Behaviors - Methods and systems for classifying mobile device behavior include generating a full classifier model that includes a finite state machine suitable for conversion into boosted decision stumps and/or which describes all or many of the features relevant to determining whether a mobile device behavior is benign or contributing to the mobile device's degradation over time. A mobile device may receive the full classifier model along with sigmoid parameters and use the model to generate a full set of boosted decision stumps from which a more focused or lean classifier model is generated by culling the full set to a subset suitable for efficiently determining whether mobile device behavior are benign. Results of applying the focused or lean classifier model may be normalized using a sigmoid function, with the resulting normalized result used to determine whether the behavior is benign or non-benign. | 12-10-2015 |
20150356463 | EXTRACTING STRUCTURED KNOWLEDGE FROM UNSTRUCTURED TEXT - Embodiments of the present invention relate to knowledge representation systems which include a knowledge base in which knowledge is represented in a structured, machine-readable format that encodes meaning. Techniques for extracting structured knowledge from unstructured text and for determining the reliability of such extracted knowledge are also described. | 12-10-2015 |
20150356464 | GENERATING DATA FROM IMBALANCED TRAINING DATA SETS - Determining a number of kernels within a model is provided. A number of kernels that include data samples of a majority data class of an imbalanced training data set is determined based on a set of generated artificial data samples for a minority data class of the imbalanced training data set. The number of kernels within the model is generated based on the set of generated artificial data samples. A likelihood of the set of generated artificial data samples being included in the majority data class of the imbalanced training data set is calculated. Parameters of each kernel in the number of kernels are updated based on the likelihood of the set of generated artificial data samples being included in the majority data class of the imbalanced training data set. Each kernel in the number of kernels is adjusted based on the updated parameters. | 12-10-2015 |
20150363709 | CLASSIFIER LEARNING DEVICE AND CLASSIFIER LEARNING METHOD - A classifier learning apparatus ( | 12-17-2015 |
20150371023 | USAGE MODELING - Embodiments of techniques or systems for usage modeling or gesture based authentication are provided herein. Actions of a user may be captured, classified, and utilized to generate an authentication scheme for a device based on gesture recognition, gesture analysis, or action analysis. Training data may be determined based on actions deemed to be training actions and one or more action models may be generated based on the training data. An action model may be indicative of how a user performs a particular or predetermined action. When a security mode is enabled, usage actions may be recorded and usage data may be extracted or determined based on the usage actions. A usage action may be identified and corresponding usage data may be compared with training data from an appropriate action model. An identity of a user associated with the usage action may be determined based on this comparison. | 12-24-2015 |
20150371139 | DEVICE LOCALIZATION BASED ON A LEARNING MODEL - Methods and systems of localizing a device are presented. In an example method, a communication signal from a device is received by a wireless reference point during a period of time. A sequence of values is generated from the communication signal, as received by the wireless reference point, during the period of time. The sequence of values is supplied to a learning model configured to generate an output based on past values of the sequence of values and at least one predicted future value of the sequence of values. The current location of the device is estimated during the period of time based on the output of the learning model. | 12-24-2015 |
20150371141 | LEAF NODE RANKING METHOD IN DECISION TREES FOR SPATIAL PREDICTION AND ITS RECORDING MEDIUM - The present inventions generally relate to a leaf node ranking method in decision trees for spatial prediction and its recording medium. The leaf node ranking method in decision trees includes a learning step to form a decision tree having one root node, in which each parent node has multiple child nodes, using training data sets for spatial prediction; and a leaf node ranking step from the decision tree that finishes the learning. In the learning step, each node of the decision tree stores both the number of classes according to class distribution of training data and structures for storing the number. In the leaf node ranking step, a rank of a leaf node is determined using the number of classes according to class distribution, which is stored in each node on a path from the root node to the leaf node. | 12-24-2015 |
20150371143 | METHOD FOR IDENTIFYING COUNTRIES VULNERABLE TO UNREST - A method for measuring and scoring susceptibility to social unrest in a country or region is disclosed. Data is collected regarding socioeconomic, political, demographic or other relevant conditions within the geographic area, a set of key indicators of possible social unrest is provided, the collected data is standardized across the indicators, performance level is calculated for each of the indicators, the collected data for each indicator within the geographic area is assigned a score based on the measurement of each indicator, and an overall score for the indicators is determined. | 12-24-2015 |
20150371147 | NOMINAL FEATURE TRANSFORMATION USING LIKELIHOOD OF OUTCOME - Embodiments of the present invention relate to transforming a nominal feature to a numeric feature that indicates a likelihood or probability of a particular outcome. Numeric features are determined that indicate a likelihood of an outcome given the value of the collected data (nominal values). Such numeric features are used to represent the corresponding nominal features for use in generating a machine learned model. As such, a nominal feature initially captured in a data set is transformed or converted to a numeric feature that represents a likelihood of a corresponding outcome as opposed to a Boolean value. Upon transforming nominal values to numeric values based on the likelihood of outcome, the numeric values can be used to generate a machine learned model that is used to predict future outcomes. | 12-24-2015 |
20150371149 | CALCULATION DEVICE, CALCULATION METHOD, AND RECORDING MEDIUM - A calculation device includes an adding unit configured to add at least one new node to a network, which has multiple nodes that output results of calculations on input data are connected and which learned a feature of data belonging to a first subclass contained in a predetermined class. The calculation device includes an accepting unit configured to accept, as input data, training data belonging to a second subclass contained in the predetermined class. The calculation device includes a calculation unit configured to calculate coupling coefficients between the new node added by the adding unit and other nodes to learn a feature of the training data belonging to the second subclass based on an output result obtained when the training data accepted by the accepting unit is input to the network. | 12-24-2015 |
20150371150 | ANALYSIS DEVICE, ANALYSIS METHOD, AND PROGRAM - An analysis device which analyzes a system that inputs input data including a plurality of input parameters and outputs output data, including an acquisition unit that acquires learning data including a plurality of sets of the input data and the output data, and a learning processing unit that learns, based on the acquired learning data, the amount of difference of output data corresponding to a difference between input parameters of two pieces of input data, an analysis method using the analysis device, and a program used in the analysis device are provided. | 12-24-2015 |
20150371151 | ENERGY INFRASTRUCTURE SENSOR DATA RECTIFICATION USING REGRESSION MODELS - A system and method are provided for physical data rectification using regression models. For example, the physical data may be energy infrastructure sensor data. The system may perform an estimation of sensor data during periods of data dropout using a regression model. The system may assess the accuracy of regression models through the comparison of probability distribution functions of physical data estimated using the regression model and actual physical data. | 12-24-2015 |
20150373043 | Collaborative and Adaptive Threat Intelligence for Computer Security - Collaborative and adaptive threat intelligence. Data collected on a first customer network is received. One or more local models are trained with at least the received data, where the one or more local models are related to security. An amount of data to transmit to a centralized controller is determined based at least on a result of the training one or more local models and the determined amount of data is transmitted to the centralized controller. Result data is received from the centralized controller that is a result of one or more global models trained on the centralized controller using data collected on multiple customer networks including the first customer network. The one or more local models are adjusted using the received result data and the one or more adjusted local models are trained. | 12-24-2015 |
20150379412 | Method and System for Forecasting - The present invention provides a forecasting engine with the ability to minimize prediction error in a preferred direction. It comprises of a receiver configured to receive training data samples. In addition, the forecasting engine includes a building module configured to build a base learner model from the training data samples. In addition, the forecasting engine includes a custom error function that emphasizes prediction error along a pre-configured direction. In addition, the forecasting engine includes an error determination module configured to determine the prediction error made by the base learner model. In addition, the forecasting engine includes an error minimization module configured to construct a new model that has lesser prediction error than the base learner model, where prediction error is as defined by the custom error function. In addition, the forecasting engine includes an iteration module that manages multiple iterations of the error determination module and the error minimization module. | 12-31-2015 |
20150379420 | METHODS FOR PROVISIONING WORKLOADS IN A STORAGE SYSTEM USING MACHINE LEARNING AND DEVICES THEREOF - A method, non-transitory computer readable medium, and provisioning advisor device that obtains an intensity and characteristics for each of a plurality of training workloads from storage device volumes. For each of the training workloads, at least first and second training workload parameters are generated, based on the training workload intensity, and an associated training workload signature is generated, based on the training workload characteristics. The first and second training workload parameters and associated training workload signatures are stored in a mapping table. A signature and an intensity for a query workload are obtained. First and second query workload parameters are determined based on a correlation of the query workload signature with the training workload signatures of the mapping table. An estimated latency for the query workload is determined, based on the first and second query workload parameters and the query workload intensity, and the estimated query workload latency is output. | 12-31-2015 |
20150379422 | Dataset Augmentation Based on Occlusion and Inpainting - Augmenting a dataset in a machine learning classifier is disclosed. One example is a system including a training dataset with at least one training data, and a label preserving transformation including an occluder, and an inpainter. The occluder occludes a selected portion of the at least one training data. The inpainter inpaints the occluded portion of the at least one training data, where the inpainting is based on data from a portion different from the occluded portion. In one example, the augmented dataset is deployed to train a machine learning classifier. | 12-31-2015 |
20150379423 | FEATURE PROCESSING RECIPES FOR MACHINE LEARNING - A first representation of a feature processing recipe is received at a machine learning service. The recipe includes a section in which groups of variables on which common transformations are to be applied are defined, and a section in which a set of transformation operations are specified. The first representation of the recipe is validated based at least in part on a library of function definitions supported by the service, and an executable version of the recipe is generated. In response to a determination that the recipe is to be executed on a particular data set, a set of provider network resources is used to implement a transformation operation indicated in the recipe. | 12-31-2015 |
20150379424 | MACHINE LEARNING SERVICE - A machine learning service implements programmatic interfaces for a variety of operations on several entity types, such as data sources, statistics, feature processing recipes, models, and aliases. A first request to perform an operation on an instance of a particular entity type is received, and a first job corresponding to the requested operation is inserted in a job queue. Prior to the completion of the first job, a second request to perform another operation is received, where the second operation depends on a result of the operation represented by the first job. A second job, indicating a dependency on the first job, is stored in the job queue. The second job is initiated when the first job completes. | 12-31-2015 |
20150379425 | CONSISTENT FILTERING OF MACHINE LEARNING DATA - Consistency metadata, including a parameter for a pseudo-random number source, are determined for training-and-evaluation iterations of a machine learning model. Using the metadata, a first training set comprising records of at least a first chunk is identified from a plurality of chunks of a data set. The first training set is used to train a machine learning model during a first training-and-evaluation iteration. A first test set comprising records of at least a second chunk is identified using the metadata, and is used to evaluate the model during the first training-and-evaluation iteration. | 12-31-2015 |
20150379426 | OPTIMIZED DECISION TREE BASED MODELS - During a training phase of a machine learning model, representations of at least some nodes of a decision tree are generated and stored on persistent storage in depth-first order. A respective predictive utility metric (PUM) value is determined for one or more nodes, indicating expected contributions of the nodes to a prediction of the model. A particular node is selected for removal from the tree based at least partly on its PUM value. A modified version of the tree, with the particular node removed, is stored for obtaining a prediction. | 12-31-2015 |
20150379427 | FEATURE PROCESSING TRADEOFF MANAGEMENT - At a machine learning service, a set of candidate variables that can be used to train a model is identified, including at least one processed variable produced by a feature processing transformation. A cost estimate indicative of an effect of implementing the feature processing transformation on a performance metric associated with a prediction goal of the model is determined. Based at least in part on the cost estimate, a feature processing proposal that excludes the feature processing transformation is implemented. | 12-31-2015 |
20150379428 | CONCURRENT BINNING OF MACHINE LEARNING DATA - Variables of observation records to be used to generate a machine learning model are identified as candidates for quantile binning transformations. In accordance with a particular concurrent binning plan generated for a particular variable, a plurality of quantile binning transformations are applied to the particular variable, including a first transformation with a first bin count and a second transformation with a different bin count. The first and second transformations result in the inclusion of respective parameters or weights for binned features in a parameter vector of the model. In a post-training phase run of the model, at least one parameter corresponding to a binned feature is used to generate a prediction. | 12-31-2015 |
20150379430 | EFFICIENT DUPLICATE DETECTION FOR MACHINE LEARNING DATA SETS - At a machine learning service, a determination is made that an analysis to detect whether at least a portion of contents of one or more observation records of a first data set are duplicated in a second set of observation records is to be performed. A duplication metric is obtained, indicative of a non-zero probability that one or more observation records of the second set are duplicates of respective observation records of the first set. In response to determining that the duplication metric meets a threshold criterion, one or more responsive actions are initiated, such as the transmission of a notification to a client of the service. | 12-31-2015 |
20150379432 | MODEL UPDATING METHOD, MODEL UPDATING DEVICE, AND RECORDING MEDIUM - A model updating method is provided. The model updating method that is executed by a computer includes calculating a score that indicates a degree of normality or abnormality of each of a plurality of pieces of data by using as a judgment model each of the pieces of data, predicting as a predicted condition whether each of the pieces of data is normal or abnormal according to score, judging whether or not the predicted condition is correct for each of the plurality of pieces of data, calculating the accuracy rate for the predicted conditions of a top specified number of pieces of data in order of decreasing abnormality as indicated by the score when the plurality of pieces of data are arranged in a specified order of score, and judging whether or not it is necessary to update the judgment model according to the accuracy rate. | 12-31-2015 |
20160004870 | Personal Security Agent - Concepts and technologies disclosed herein are directed to a personal security agent. According to one aspect disclosed herein, a compute resource includes a processor that can execute the personal security agent to perform operations. The compute resource can receive data from a data source. The compute resource can receive a job request to provide security for an entity. The job request can include a job requirement. The compute resource can analyze the job requirement and the data to determine an action. The compute resource can provide instructions for executing the action to a controller domain. The controller domain can execute the action in at least partial fulfillment of the job requirement. | 01-07-2016 |
20160004970 | METHOD AND APPARATUS FOR RECOMMENDATIONS WITH EVOLVING USER INTERESTS - A user has an inherent predisposition to have an interest for a particular item. The user's interests may also be affected by what people in her social circle are interested in. To more accurately make recommendations, a user's inherent interests, social influence, how a user responds to recommendations, and/or the user's desire for novelty are taken into consideration. Considering the evolution of users' interests in response to the users' social interactions and users' interactions with the recommender system, the recommendation problem is formulated as an optimization problem to maximize the overall expected utilities of the recommender system. Tractable solutions to the optimization problem are presented for some use cases: (1) when the system does not perform personalization; (2) when the users in the system exhibit attraction dominant behavior; and (3) when the users in the system exhibit aversion dominant behavior. | 01-07-2016 |
20160004971 | NETWORK SERVER ARRANGEMENTS FOR PROCESSING NON-PARAMETRIC, MULTI-DIMENSIONAL, SPATIAL AND TEMPORAL HUMAN BEHAVIOR OR TECHNICAL OBSERVATIONS MEASURED PERVASIVELY, AND RELATED METHODS FOR THE SAME - Network server arrangement for processing non-parametric, multi-dimensional, spatial and temporal human behavior or technical observations measured pervasively, and related method for the same are disclosed. An example computer system to process usage data received from a wireless device, the computer system including a memory including machine readable instructions; and a processor to execute the instructions to: process the usage data to identify applications which were sequentially accessed on the wireless device in a time period; build a behavior model based on the identified applications, the behavior model to describe user behavior associated with the wireless device; execute the behavior model to predict usage of an application on the wireless device; based on the prediction, monitor usage of the wireless device to determine an accuracy of the prediction; and update the behavior model based on the accuracy of the prediction. | 01-07-2016 |
20160004977 | Content Monetization System - A system and method are provided to monetize content by redacting the content with machine learning algorithms. This invention increases the conversion rate of website surfers to paid customers. Extracted texts of the content are tokenized and then scored with normalized value [0, 1] to measure their significance. Intra-token, inter-token, extra-token, and tagged token features are used to characterize each individual token. Scores of sentences, paragraphs, sections, and even chapters can be calculated with various methods based on the scores of tokens. Then, the content is redacted according to the calculated scores. Customers can view the redacted content for free. If interested, they can purchase the content and view the full, non-redacted version of the content. The present invention is useful in publication and monetization of digital contents such as e-books. | 01-07-2016 |
20160004978 | AUTOMATIC DETECTION OF ANOMALIES IN GRAPHS - A method, apparatus and product for automatic detection of anomalies in graphs. The method comprising obtaining training data, the training data comprising a plurality of graphs, each defined by nodes and edges connecting between the nodes, at least some of the nodes are labeled; determining a statistical model of a graph in accordance with the training data, the statistical model takes into account at least one structured and labeled feature of the graph, wherein the structured and labeled feature of the graph is defined based on a connection between a plurality of nodes and based on at least a portion of the labels of the plurality of nodes; obtaining an examined graph; and determining a score of the examined graph indicative of a similarity between the examined graph and the training data, wherein the score is based on a value of the structured and labeled feature in the examined graph. | 01-07-2016 |
20160004979 | MACHINE LEARNING - A method, relating to machine learning, may receive information from a plurality of devices, process the received information to create processed information, determine, based on utilizing a particular analysis technique, a plurality of triggering parameters associated with the processed information, calculate, based on determining the plurality of triggering parameters, a response parameter, and transmit the response parameter to another device, causing the other device to perform an action, where a result of the action being used as part of a machine learning process. | 01-07-2016 |
20160012192 | SYSTEM AND METHOD FOR DETERMINING TRIAGE CATEGORIES | 01-14-2016 |
20160012333 | DATA CLASSIFICATION METHOD, STORAGE MEDIUM, AND CLASSIFICATION DEVICE | 01-14-2016 |
20160012337 | INFERENCE ELECTRONIC SHELF LIFE DATING SYSTEM FOR PERISHABLES | 01-14-2016 |
20160012348 | TOUCH CLASSIFICATION | 01-14-2016 |
20160012349 | LEARNING SYSTEM AND METHOD FOR CLINICAL DIAGNOSIS | 01-14-2016 |
20160012350 | INTEROPERABLE MACHINE LEARNING PLATFORM | 01-14-2016 |
20160012351 | INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING METHOD, AND PROGRAM | 01-14-2016 |
20160012352 | Data Processing Method and Computer System | 01-14-2016 |
20160019462 | Predicting and Enhancing Document Ingestion Time - A mechanism is provided in a data processing system for predicting and enhancing ingestion time for a set of input documents. The mechanism receives a set of documents to be added to a corpus of the data processing system. The mechanism records document features of each document within the set of documents using an annotation engine within the data processing system. The mechanism predicts an ingestion time for each document within the set of documents based on the document characteristics and a machine learning model. The mechanism assigns the set of documents to data processing system resources to be processed based on the predicted ingestion time for each document. | 01-21-2016 |
20160019466 | EVENT DETECTION THROUGH TEXT ANALYSIS USING TRAINED EVENT TEMPLATE MODELS - A system and method for detecting events based on input data from a plurality of sources. The system may receive input from a plurality of sources containing information about possible events. A method for event detection involves pre-processing and normalizing a data input from a plurality of sources, extracting and disambiguating events and entities, associate event and entities, correlate events and entities associated from a data input to results from a different data sources to determine if an event has occurred, and store the detected events in a data storage. | 01-21-2016 |
20160019469 | PARALLEL RETRIEVAL OF TRAINING DATA FROM MULTIPLE PRODUCERS FOR MACHINE LEARNING SYSTEMS - A sorting engine is an intermediary layer between a multi-threaded engine that obtains batches of training data from the producers in parallel and the underlying machine learning engine. The sorting engine includes a shared buffer that has various slots for storing batches of training data, where the slots are organized in a deterministic order associated with the producers. A batch of training data obtained by a thread from a given producer may be stored only in a corresponding slot in the shared buffer. Further, the sorting engine transmits the batch to the machine learning engine only when a previous batch in the deterministic order has been transmitted from the shared buffer to the machine learning engine. | 01-21-2016 |
20160019470 | EVENT DETECTION THROUGH TEXT ANALYSIS USING TRAINED EVENT TEMPLATE MODELS - A system and method for detecting events based on input data from a plurality of sources. The system may receive input from a plurality of sources containing information about possible events. A method for event detection involves pre-processing and normalizing a data input from a plurality of sources, extracting and disambiguating events and entities, associate event and entities, correlate events and entities associated from a data input to results from a different data sources to determine if an event has occurred, and store the detected events in a data storage. | 01-21-2016 |
20160019471 | AUTOMATIC TASK CLASSIFICATION BASED UPON MACHINE LEARNING - A system and method is provided that processes a training database of human-generated requests in each of a plurality of task categories with a machine learning algorithm to develop a task classifier model that may be applied to subsequent user requests to determine the most likely one of the task categories for the subsequent user request. | 01-21-2016 |
20160026354 | SYSTEM AND METHOD FOR ADAPTIVE INDIRECT MONITORING OF SUBJECT FOR WELL-BEING IN UNATTENDED SETTING - A system is provided for event-based monitoring of a subject's well-being within an unattended setting. A plurality of sensors are disposed within the setting for sensing disparate events, and an analytics processing portion is coupled to the sensors to collectively acquire sensing data therefrom, and map a plurality of sensed data points for a selected combination of disparate events to a conduct adaptively characterized for the subject. The mapping occurs according to a set of pre-established reference event patterns, relative to which each characterized conduct is screened for excessive aberration. The analytics processing portion actuates generation of a graphic user interface displaying at least one reporting page. The reporting page contains for each characterized conduct certain graphic indicia determined responsive to the screening thereof. At least one wirelessly coupled monitoring device actuates responsive to the analytics processing portion to render the graphic user interface for a remotely monitoring user. | 01-28-2016 |
20160026916 | INFERRED SALARY DISTRIBUTION FOR SCHOOLS - Systems, methods and a machine-readable media are described herein for a salary range engine to identify at least one attribute of a first member profile from a plurality of member profiles of a social networking service. The salary range engine correlates the at least one attribute with respect to at least a portion of trained salary data in a trained salary data repository. The salary range engine infers a target salary range based on a correlation between the at least one attribute of the first member profile and at least the portion of the trained salary data. | 01-28-2016 |
20160026919 | SYSTEM AND METHOD FOR SOCIAL EVENT DETECTION - A computer-implemented method, computer program product, and systems for event detection. The computer system for event detection includes an interface component configured to receive data entries from a social media data storage wherein the data entries have associated time values and location values. The received data entries are stored in a data storage component. A cluster creator of a clustering component can create a cluster with cluster data entries wherein the cluster data entries are received data entries having time values within a range of a time interval and having location values within a range of a location interval. A cluster evaluator can then determine a cluster value for the cluster by computing an event-specific cluster feature vector as input to a machine learning algorithm wherein the machine learning algorithm calculates the cluster value. If the cluster value exceeds an event detection threshold value an event is detected. | 01-28-2016 |
20160026921 | Synchronization for Context-Aware Complex Event Processing - A complex event processing system comprises one or more rule engines configured to receive information from a source system via a message broker. Multiple rule engines may be used in parallel, with the same/different rules deployed. According to an embodiment, a rule engine may include a manager component, a proxy component, a reasoner component, and a working memory. The manager and proxy serve as interfaces with the message broker to allow asynchronous communication with a provider storing state information. The reasoner is configured to execute rules based upon occurrence of events in the source system. Embodiments may be particularly suited to implementing a gamification platform including a business entity provider, with an existing business source system (e.g. CRM, ERP). | 01-28-2016 |
20160026922 | Distributed Machine Learning Autoscoring - In one embodiment, a management system determines respective capability information of machine learning systems, the capability information including at least an action the respective machine learning system is configured to perform. The management system receives, for each of the machine learning systems, respective performance scoring information associated with the respective action, and computes a degree of freedom for each machine learning system to perform the respective action based on the performance scoring information. Accordingly, the management system then specifies the respective degree of freedom to the machine learning systems. In one embodiment, the management system comprises a management device that computes a respective trust level for the machine learning systems based on receiving the respective performance scoring feedback, and a policy engine that computes the degree of freedom based on receiving the trust level. In further embodiments, the machine learning system performs the action based on the degree of freedom. | 01-28-2016 |
20160026924 | METHOD AND SYSTEM FOR IDENTIFYING GRAPHICAL MODEL SEMANTICS - A system and method for identifying graphical model semantics, one aspect, receive a graphical diagram, associate each of a plurality of elements with at least one predetermined meta-types, identify a plurality of types in the graphical diagram, and determine a category for each of elements in said graphical diagram. Containment identification rules identify one or more containment relationships in the graphical diagram. Multiplicity identification rules identify multiplicity relationships in the graphical diagram. Advanced semantic rules identify visual elements that represent attributes and refine relationships to identify unique behavior. | 01-28-2016 |
20160026925 | OVERLAPPING TRACE NORMS FOR MULTI-VIEW LEARNING - In multi-view learning, optimized prediction matrices are determined for V≧2 views of n objects, and a prediction of a view of an object is generated based on the optimized prediction matrix for that view. An objective | 01-28-2016 |
20160026926 | CLOTHING MATCHING SYSTEM AND METHOD - The invention provides a clothing matching system and a method for generating at least an outfit suggestion comprising the steps of determining a colour classification of a user, providing a plurality of articles of clothing, selecting a dress code based on the user preference and generating at least one outfit suggestion based on the colour classification of the user, the plurality of clothing items and the selected dress code. | 01-28-2016 |
20160026931 | System and Method for Providing a Machine Learning Re-Training Trigger - A system and method that records the important words lists according to a previous naive Bayes classifier for each category. If a new document provides different important words to distinguish the category from other categories, the method would then re-train the system. If the new document provides the same important words as the related important words list, the method would not re-train the system. When there are new training examples, the method must re-train the system. If the training examples come into the system one by one, the method must re-train the system again and again. Two re-training policies are taught to make the system of the present invention more effective and keep it up to date. The first policy is to regularly re-train the system everyday with all training examples. The second policy is to re-train in real time, during intervals each day. | 01-28-2016 |
20160026932 | Intelligent System with Integrated Representation Learning and Skill Learning - A computer-implemented method includes, in one aspect, obtaining data specifying one or more expressions for a problem to be solved and an action that changes a state of the problem when applied to the one or more expressions, identifying one or more features of the one or more expressions, identifying a precondition for applying the action that changes the state of the problem, identifying a sequence of operator functions, and generating a production rule based on the identified one or more features, the identified precondition, and the identified operator function. | 01-28-2016 |
20160026933 | AUTOMATED DEFECT AND OPTIMIZATION DISCOVERY - Performance information and configuration information is received for the plurality of computer systems. The computer systems are grouped into a plurality of clusters based at least in part on the performance information, where the plurality of clusters includes a first cluster and a second cluster. A system configuration associated with the first cluster is automatically identified from the configuration information and is automatically sent to the second cluster. | 01-28-2016 |
20160034460 | METHOD AND SYSTEM FOR RANKING MEDIA CONTENTS - A method is provided for ranking media contents. The method includes receiving media contents through a network and extracting feature values of the received media contents. The method also includes implementing a parameter reinforcement learning process to obtain automatically distribution over relativeness and irrelativeness of the received media contents. Further, the method includes ranking the received media contents by a multi-armed bandit algorithm based on the obtained distribution over relativeness and irrelativeness of the received media contents. | 02-04-2016 |
20160034651 | A METHOD FOR IMPROVING DISEASE DIAGNOSIS USING MEASURED ANALYTES - Methods for improving clinical diagnostic tests are provided, along with associated diagnostic techniques. | 02-04-2016 |
20160034813 | METHOD FOR COUNTING NUMBER OF PEOPLE BASED ON APPLIANCE USAGES AND MONITORING SYSTEM USING THE SAME - A method for counting a number of people based on appliance usages and a monitoring system using the same method. The method includes the following steps: collecting first numbers of people and first appliance usages corresponding to a first time duration in a specific space; establishing a predictive model related to the first time duration according to the first numbers of people and the appliance usages; detecting a second appliance usages in a second time duration; predicting a second number of people corresponding to the second time duration and the second appliance usages according to the predictive model. | 02-04-2016 |
20160034814 | NOISE-BOOSTED BACK PROPAGATION AND DEEP LEARNING NEURAL NETWORKS - A learning computer system may update parameters and states of an uncertain system. The system may receive data from a user or other source; process the received data through layers of processing units, thereby generating processed data; process the processed data to produce one or more intermediate or output signals; compare the one or more intermediate or output signals with one or more reference signals to generate information indicative of a performance measure of one or more of the layers of processing units; send information indicative of the performance measure back through the layers of processing units; process the information indicative of the performance measure in the processing units and in interconnections between the processing units; generate random, chaotic, fuzzy, or other numerical perturbations of the received data, the processed data, or the one or more intermediate or output signals; update the parameters and states of the uncertain system using the received data, the numerical perturbations, and previous parameters and states of the uncertain system; determine whether the generated numerical perturbations satisfy a condition; and if the numerical perturbations satisfy the condition, inject the numerical perturbations into one or more of the parameters or states, the received data, the processed data, or one or more of the processing units. | 02-04-2016 |
20160034823 | Methods, Systems, And Computer Program Products For Optimizing A Predictive Model For Mobile Network Communications Based On Historical Context Information - Methods and systems are described for optimizing a predictive model for mobile network communications based on historical context information. In one aspect, historical context information is collected including at least one of communication environment, communication parameter estimates, mobile device statistics, mobile device transmit settings, base station receiver settings, past network statistics and settings, and adjacent network node information statistics and settings, the historical context information including data from communications of at least one mobile device. A predictive model for network communications is determined based on the historical context information. A communication context for a first mobile device different than the at least one mobile device is determined. The first device is scheduled and/or network parameters are set based on the determined predictive model. Actual to expected results are compared and the predictive model is adjusted based on the comparison. | 02-04-2016 |
20160034824 | AUTO-ANALYZING SPATIAL RELATIONSHIPS IN MULTI-SCALE SPATIAL DATASETS FOR SPATIO-TEMPORAL PREDICTION - A method and system to perform spatio-temporal prediction are described. The method includes obtaining, based on communication with one or more sources, multi-scale spatial datasets, each of the multi-scale spatial datasets providing a type of information at a corresponding granularity, at least two of the multi-scale spatial datasets providing at least two types of information at different corresponding granularities. The method also includes generating new features for each of the multi-scale spatial datasets, the new features being based on features of each of the multi-scale spatial datasets and spatial relationships between and within the multi-scale spatial datasets. The method further includes selecting, using the processor, features of interest from among the new features, training a predictive model based on the features of interest, and predicting an event based on the predictive model. | 02-04-2016 |
20160034825 | SYSTEMS AND METHODS FOR SOLVING UNRESTRICTED INCREMENTAL CONSTRAINT PROBLEMS - We present the architecture of a high-performance constraint solver R-Solve that extends the gains made in SAT performance over the past fifteen years on static decision problems to problems that require on-the-fly adaptation, solution space exploration and optimization. R-Solve facilitates collaborative parallel solving and provides an efficient system for unrestricted incremental solving via Smart Repair. R-Solve can address problems in dynamic planning and constrained optimization involving complex logical and arithmetic constraints. | 02-04-2016 |
20160042276 | METHOD OF AUTOMATED DISCOVERY OF NEW TOPICS - The present disclosure relates to a method for performing automated discovery of new topics from unlimited documents related to any subject domain, employing a multi-component extension of Latent Dirichlet Allocation (MC-LDA) topic models, to discover related topics in a corpus. The resulting data may contain millions of term vectors from any subject domain identifying the most distinguished co-occurring topics that users may be interested in, for periodically building new topic ID models using new content, which may be employed to compare one by one with existing model to measure the significance of changes, using term vectors differences with no correlation with a Periodic New Model, for periodic updates of automated discovery of new topics, which may be used to build a new topic ID model in-memory database to allow query-time linking on massive data-set for automated discovery of new topics. | 02-11-2016 |
20160042283 | Detector Devices for Presenting Notifications and Supporting Context Inferences - Techniques can relate to generating inferences based on network devices' measuring of environmental data points and generating notifications or controlling devices based on the inferences. One or more environmental data points can be accessed. Each environmental data point in the one or more environmental data points can include one measured by a detector device and that characterizes a corresponding environmental stimulus. At least one of the environmental data points can be indicative of a light intensity or power usage measured by a first device. An inference can be generated based on the one or more environmental data points. A notification or device control can be identified based on the inference. A communication can be generated and transmitted to a second device. Receipt of the communication can cause the second device to present the notification or to be controlled in accordance with the device control. | 02-11-2016 |
20160042290 | ANNOTATION PROBABILITY DISTRIBUTION BASED ON A FACTOR GRAPH - In order to address annotation bias in batch annotations, obtained via crowdsourcing, on a set of comments on user posts in a social network, a system determines an annotation probability distribution based on a factor-graph model of the batch annotations. In particular, during operation the system computes the factor-graph model that represents relationships between feature vectors that represent the comments and the annotations for the comments. Note that, for a given batch of k comments, the factor-graph model may include a statistically dependent combination of statistically independent models of the interrelationships between the feature vectors and the annotations for the k comments. Then, the system calculates the annotation probability distribution based on model parameters associated with the factor-graph model, a mapping function that maps from the feature vectors to the annotations, and an indicator function that represents the annotations for the comments in the batches. | 02-11-2016 |
20160042292 | AUTOMATED METHODOLOGY FOR INDUCTIVE BIAS SELECTION AND ADAPTIVE ENSEMBLE CHOICE TO OPTIMIZE PREDICTIVE POWER - A computer-implemented method of automating inductive bias selection includes a computer receiving a plurality of examples, each example providing a plurality of feature-value pairs. The computer constructs an inductive bias dataset which correlates each respective example in the plurality of examples with numerical indications of training quality. The numerical indications of training quality for each respective example are generated by creating a plurality of models, with each model corresponding to a distinct set of inductive biases. The training quality for each respective model is evaluated when applied to the respective example. The computer uses the inductive bias dataset to select a plurality of inductive biases for application to one or more new datasets. | 02-11-2016 |
20160042295 | SUPPORT VECTOR MACHINE COMPUTATION - A technique solves an SVM problem on table J, defined as the join of two tables T | 02-11-2016 |
20160042298 | CONTENT DISCOVERY AND INGESTION - Knowledge automation techniques may include discovering data files from one or more content repositories, and identifying key terms in the data files. For each of the identified key terms, a frequency of occurrence of the key term in the corresponding data file, and locations of the key term in the corresponding data file can be determined. A plurality of knowledge units can be generated from the data files based on the determined frequencies of occurrence and the determined locations of the key terms. | 02-11-2016 |
20160042299 | IDENTIFICATION AND BRIDGING OF KNOWLEDGE GAPS - Knowledge automation techniques may include techniques may include monitoring search queries for content in one or more data stores performed by a plurality of users, and identifying, based on the search queries, a set of one or more search terms. A frequency count for each search term based on a number of occurrence of the search term in the search queries can be determined, and search results corresponding to the search queries can be analyzed. The techniques may include determining, based on the frequency count of each search term and the user responses to the search results, a knowledge gap indicating a lack of content associated with a particular search term in the one or more data stores. The techniques may also include identifying a content source to fill the knowledge gap. | 02-11-2016 |
20160048566 | TECHNIQUES FOR INTERACTIVE DECISION TREES - Techniques for providing interactive decision trees are included. For example, a system is provided that stores data related to a decision tree, wherein the data includes one or more data structures and one or more portions of code. The system receives input corresponding to an interaction request associated with a modification to the decision tree. The system determines whether the modification requires multiple-processing iterations of the distributed data set. The system generates an application layer modified decision tree when the generating requires no multiple-processing iterations of the distributed data set. The system facilitates server layer modification of the decision tree when the modification requires multiple-processing iterations of the distributed data set. The system generates a representation of the application layer modified decision tree or the server layer modified decision tree. | 02-18-2016 |
20160048648 | Method for Providing Healthcare Industry Optimized Cognitive Insights - A method for providing healthcare optimized cognitive insights comprising: receiving data from a plurality of data sources, at least some of the plurality of data sources comprising healthcare relevant data sources; processing the data from the plurality of data sources to provide cognitively processed insights; performing a learning operation to iteratively improve the cognitively processed insights over time; and, providing the cognitively processed healthcare relevant insights to a destination. | 02-18-2016 |
20160048761 | Healthcare Industry Optimized Cognitive Information Processing System Environment - A cognitive information processing system environment comprising: a plurality of data sources, at least some of the plurality of data sources comprising healthcare relevant data sources; a cognitive inference and learning system coupled to receive a data from the plurality of data sources, the cognitive inference and learning system processing the data from the plurality of data sources to provide cognitively processed healthcare relevant insights, the cognitive inference and learning system further comprising performing a learning operation to iteratively improve the cognitively processed healthcare relevant insights over time; and, a destination, the destination receiving the cognitively processed healthcare relevant insights. | 02-18-2016 |
20160048762 | Hybrid Data Architecture for Use Within a Healthcare Industry Optimized Cognitive Environment - A data architecture for use within a cognitive information processing system environment comprising: a plurality of data sources, the plurality of data sources comprising a public data source and a private data source, the public data source comprising publicly available healthcare information, the private data source comprising privately managed, company specific healthcare information; and, a cognitive data management module, the cognitive data management module accessing information from the plurality of data sources and providing the information to an inference and learning system. | 02-18-2016 |
20160048763 | Method for Using Hybrid Data Within a Healthcare Industry Optimized Cognitive Environment - A method for using hybrid data within a cognitive information processing system environment comprising: receiving data from a plurality of data sources, the plurality of data sources comprising a public data source and a private data source, the public data source comprising publicly available healthcare information, the private data source comprising privately managed, company specific healthcare information; accessing information from the plurality of data sources via a cognitive data management module; and, providing the information to an inference and learning system. | 02-18-2016 |
20160048766 | METHOD AND SYSTEM FOR GENERATING AND AGGREGATING MODELS BASED ON DISPARATE DATA FROM INSURANCE, FINANCIAL SERVICES, AND PUBLIC INDUSTRIES - A method and system for making financial or medical decisions. The method comprises training sets of models using classification training with sets of data derived from segregated data sources. Overall weighting of each model within the sets of models are determined for each of the sub-datasets. The sets of models, the overall weighting of each model and a number of examples provided from the data for each of the datasets are transmitted to a central server over a communication network, wherein the central server is configured to determine the relative weights of each of the sets of models in the overall ensemble model based on the number of examples, combine the sets of models, receive new application data, and predict at least one of outcome variables, an uncertainty factor for the variables, and drivers of the outcome variables based on the new application data. | 02-18-2016 |
20160048770 | ENTITY RESOLUTION INCORPORATING DATA FROM VARIOUS DATA SOURCES - A pair of records is tokenized to form a normalized representation of an entity represented by each record. The tokens are correlated to a machine learning system by determining whether a learned resolution already exists for the two entities. If not, the normalized records are compared to generate a comparison measure to determine whether the records match. The normalized records can also be used to perform a web search and web search results can be normalized and used as additional records for matching. When a match is found, the records are updated to indicate that they match, and the match is provided to the machine learning system to update the learned resolutions. | 02-18-2016 |
20160048771 | DISTRIBUTED STAGE-WISE PARALLEL MACHINE LEARNING - A method for machine learning a data set in a data processing framework is disclosed. A forest is trained with the data set that generates a plurality of trees in parallel. Each tree includes leaf nodes having a constant weight. A discriminative value for each leaf node is learned with a supervised model. The forest is reconstructed with the discriminative values replacing the constant weight for each leaf node. | 02-18-2016 |
20160048773 | ENTITY ANALYSIS SYSTEM - A method for building a factual database of concepts and entities that are related to the concepts through a learning process. Training content (e.g., news articles, books) and a set of entities (e.g., Bill Clinton and Barack Obama) that are related to a concept (e.g., Presidents) is received. Groups of words that co-occur frequently in the textual content in conjunction with the entities are identified as templates. Templates may also be identified by analyzing parts-of-speech patterns of the templates. Entities that co-occur frequently in the textual content in conjunction with the templates are identified as additional related entities (e.g., Ronald Reagan and Richard Nixon). To eliminate erroneous results, the identified entities may be presented to a user who removes any false positives. The entities are then stored in association with the concept. | 02-18-2016 |
20160055132 | AUTOMATED CUSTOMIZED WEB PORTAL TEMPLATE GENERATION SYSTEMS AND METHODS - An automated Web portal template generation method includes parsing, via a parser subsystem, a number of Webpages of a first Website from which a Web portal template to be customized is to be accessed. The method further includes producing an entity feature set for the first Website based on a result of the parsing and processing the entity feature set for the first Website via a classifier subsystem to produce a set of data that represents, for each of a plurality of entities, a respective probability of the entity belonging to a respective one of a plurality of classes. The method additionally includes performing, by a color matching subsystem, color matching on the set of data produced by the classifier subsystem to generate a number of proposed color combinations for a proposed customization of the Web portal template. | 02-25-2016 |
20160055416 | PREDICTING A CONSUMER SELECTION PREFERENCE BASED ON ESTIMATED PREFERENCE AND ENVIRONMENTAL DEPENDENCE - An information processing apparatus includes a history acquisition section configured to acquire history data including a history indicating that a plurality of selection subjects have selected selection objects; a learning processing section configured to allow a choice model to learn a preference of each selection subject for a feature and an environmental dependence of selection of each selection object in each selection environment using the history data, where the choice model uses a feature value possessed by each selection object, the preference of each selection subject for the feature, and the environmental dependence indicative of ease of selection of each selection object in each of a plurality of selection environments to calculate a selectability with which each of the plurality of selection subjects selects each selection object; and an output section configured to output results of learning by the learning processing section. | 02-25-2016 |
20160055419 | IDENTIFYING ELECTRIC VEHICLE OWNERS - The subject disclosure relates to methods and systems for identifying and classifying electric-vehicle (EV) owners. Methods of the subject technology can include steps for generating an initial model based on a plurality of load-curve characteristics, and training the initial model using a training data set to produce a configured model. In some implementations, the methods can also include steps for determining a probabilistic classification for each of a second plurality of users by analyzing load-curve data associated with the second plurality of users using the configured model. Systems and computer readable media are also provided. | 02-25-2016 |
20160055422 | APPARATUS AND METHOD FOR GAMIFICATION OF SENSOR DATA INTERPRETATION IN SMART HOME - An apparatus and method for correlating events in a smart home system as a pattern. The apparatus and method include collecting from smart home devices, state change events of the smart home system, determining whether a series of the collected state change events are a known pattern, requesting, when the series of the collected state change events is an unknown pattern, users of the smart home system to identify what caused the collected state change events, and judging, by the smart home users, a best reason among the identified causes of the collected state change events. | 02-25-2016 |
20160055423 | INFLUENCE FILTERING IN GRAPHICAL MODELS - According to an aspect, influence filtering in a graphical model includes accessing the graphical model in a data store. The graphical model includes a plurality of nodes connected by edges having edge strengths representing a degree of relation between the nodes. A target relation strength for a pair of nodes in the graphical model is received. An edge strength of an edge in a direct path between the pair of nodes is determined by traversing, in the graphical model, one or more paths other than the direct path between the pair of nodes. The determining also includes estimating a cumulative strength of the traversed paths, and calculating the edge strength for the edge in the direct path based on the cumulative strength of the traversed paths and the target relation strength. The calculated edge strength is assigned to the edge in the direct path between the two nodes. | 02-25-2016 |
20160055424 | INTELLIGENT HORIZON SCANNING - A method and computer program product and tool for increasing efficiency of an intelligent horizon scanning process. The horizon scanning process methodology uses a set of negative training examples, a universum data set of articles, and a data subset of unlabeled instances from received positive class and unlabeled electronic documents. Further a ranking model that can use partial pairwise preferences is implemented to generate a list of recommended articles for output to a user. | 02-25-2016 |
20160055425 | ROBOT SYSTEM, ROBOT TEACHING METHOD AND CONTROL DEVICE THEREFOR - A robot system includes a robot including a robot arm, and a first hand and a second hand which are connected to the robot arm and which are provided to independently rotate about an axis on the robot arm; and a controller configured to control an operation of the robot. When the robot arm and the first hand are operated so that the first hand reaches a predetermined target position, teaching values for the first hand in the target position is generated. When the first hand and the second hand are rotated based on the teaching values for the first hand, a relative error in rotation amount around the axis between the first hand and the second hand is acquired and stored in a memory. Teaching values for the second hand is generated from the teaching values for the first hand based on the acquired relative error. | 02-25-2016 |
20160055426 | CUSTOMIZABLE MACHINE LEARNING MODELS - Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for customizable machine learning models. In some implementations, data is received, including (i) example data sets and (ii) data specifying one or more criteria to be assessed. A set of multiple models is trained, where each model in the set of models is trained using a training data set comprising a different subset of the example data sets. Output of the models is obtained for various example data sets, and a combination of n-grams is selected based on the outputs. The example data sets are used to train a classifier to evaluate input data with respect to the specified one or more criteria based on whether the input data includes the n-grams in the selected combination of n-grams. | 02-25-2016 |
20160055427 | METHOD FOR PROVIDING DATA SCIENCE, ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING AS-A-SERVICE - An automated method of predictive model development first cleans up raw supervised and unsupervised training data with a step that uses an algorithm to make every field of every record consistent, cohesive, and productive. Then the resulting flat data is given texture in a next step by a data enrichment algorithm that culls fields that do not contribute to predictive model building and that adds new fields computed from data combinations that are tested to add value to later steps that build different types of predictive models. Another late step for building smart-agents and their entity profiles uses another algorithm that benefits greatly from the cleaned and highly enriched training data. The predictive models and smart-agents and their entity profiles are then rendered as deliverable predictive model markup language documents in a final step executed by a specialized algorithm. | 02-25-2016 |
20160057041 | AUTOMATIC REMEDIATION OF POOR-PERFORMING VIRTUAL MACHINES FOR SCALABLE APPLICATIONS - A management system and method for remediating poor-performing clients running in a distributed computer system uses a machine learning technique to automatically detect one or more poor-performing clients among a plurality of clients running in the distributed computer based on at least performance data and resource usage data of the clients. An action is then initiated to mitigate the effects of the poor-performing clients. | 02-25-2016 |
20160063209 | SYSTEM AND METHOD FOR HEALTH CARE DATA INTEGRATION - Systems and methods for integrating data from various sources are provided, the system comprising a processor and a non-transitory computer readable storage medium storing instructions which when executed by the processor, configure the processor to filter and transform data received from one or more health care organizations by: receiving one or more data sets; developing one or more rules based upon the one or more data sets; and applying the one or more rules to the one or more data sets to detect the presence of one or more data elements. | 03-03-2016 |
20160063384 | SYSTEM FOR BUILDING AND DEPLOYING INFERENCE MODEL - A system and related method for building and deploying one or more inference models for use in remote condition monitoring of a first fleet of a first asset. The system includes model configuration data for subsequent use by a model builder application to construct one or more desired inference models for the first asset. The model configuration data is customized to the first asset and the desired one or more inference models, and is provided in a format which is easily readable and editable by a user of the system. The model configuration data is separate from the underlying processing algorithms which are employed by the model builder application in the constructing of the one or more desired inference models during a learning mode of operation of the system. | 03-03-2016 |
20160063386 | AUTOMATIC RULE COACHING - A method of validating rules configured to be utilized in an information extraction application, including: receiving a plurality of labeled samples in a training database; for each of the rules in the rule database: (a) determining, for each of the data points of the plurality of labeled samples in the training database to which the rule applies, whether applying the rule to the data point has a positive or negative impact on matching an output for the data point based on the rule to the assured output of the labeled sample corresponding to the data point; (b) generating positive impact information for the rule based on the positive voters; (c) generating negative impact information for the rule based on the negative voters; and (d) determining a metric for the rule based on the quantity of the negative voters and the quantity of the positive voters; ranking the rules based on the metrics corresponding to the rules; and sending to a user for refinement one or more flagged rules of the rules that have a lowest ranking of the metric. Other embodiments are provided. | 03-03-2016 |
20160063389 | SYSTEMS AND METHODS FOR PARTITIONING SETS OF FEATURES FOR A BAYESIAN CLASSIFIER - The technology disclosed relates to methods for partitioning sets of features for a Bayesian classifier, finding a data partition that makes the classification process faster and more accurate, while discovering and taking into account feature dependence among sets of features in the data set. It relates to computing class entropy scores for a class label across all tuples that share the feature-subset and arranging the tuples in order of non-decreasing entropy scores for the class label, and constructing a data partition that offers the highest improvement in predictive accuracy for the data set. Also disclosed is a method for partitioning a complete set of records of features in a batch computation, computing increasing predictive power; and also relates to starting with singleton partitions, and using an iterative process to construct a data partition that offers the highest improvement in predictive accuracy for the data set. | 03-03-2016 |
20160063393 | LOCALIZED LEARNING FROM A GLOBAL MODEL - Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for obtaining a global model for a particular activity, the global model derived based on input data representing multiple observations associated with the particular activity performed by a collection of users; determining, using the global model, expected data representing an expected observation associated with the particular activity performed by a particular user; receiving, by a computing device operated by the particular user, particular data representing an actual observation associated with the particular activity performed by the particular user; determining, by the computing device and using (i) the expected data and (ii) the particular data, residual data of the particular user; and deriving a local model of the particular user based on the residual data. | 03-03-2016 |
20160063394 | Computing Device Classifier Improvement Through N-Dimensional Stratified Input Sampling - Discrete sets of data are divided into collections in accordance with strata delineated along multiple dimensions of data. Each dimension of data represents criteria to be evaluated and the stratification of a dimension is based on a distribution of the discrete sets of data along such a dimension. Once divided into the multidimensional strata, one or more discrete sets of data are randomly selected from each stratum and are provided to human judges to generate corresponding classifications of such a discrete set of data. Such human-generated classifications are compared with computer-generated classifications associated with the same discrete sets of data for purposes of evaluating the computer-implemented functionality generating such classifications. Such human-generated classifications are also associated with the corresponding discrete sets of data for purposes of training, and thereby improving, computer-implemented functionality. | 03-03-2016 |
20160063395 | METHOD AND APPARATUS FOR LABELING TRAINING SAMPLES - Provided in the present invention are a method and apparatus for labeling training samples. In the embodiments of the present invention, two mutually independent classifiers, i.e. a first classifier and a second classifier, are used to perform collaborative forecasting on M unlabeled first training samples to obtain some of the labeled first training samples, without the need for the participation of operators; the operation is simple and the accuracy is high, thereby improving the efficiency and reliability of labeling training samples. | 03-03-2016 |
20160063396 | METHOD AND APPARATUS FOR CLASSIFICATION - The present invention provides a method and apparatus for classification. In the embodiments of the present invention, data to be predicted is input into M target classifiers respectively, so as to obtain the predicted result output by each target classifier of the M target classifiers, where M is an integer greater than or equal to 2, and each of the target classifiers is independent of another, so that a classification result of the data can be obtained according to the predicted result output by each of the target classifiers and a prediction weight of each of the target classifiers; and since each target classifier of the M target classifiers is independent of another, the classification result of the data can be obtained by making full use of the classification capability of each target classifier, thus improving the accuracy of the classification result. | 03-03-2016 |
20160063397 | MACHINE-LEARNING SYSTEM FOR OPTIMISING THE PERFORMANCE OF A BIOMETRIC SYSTEM - Summarizing, the application relates to a machine-learning system for adaptively changing a matching threshold of a biometric system. The machine-learning system comprises a batch aggregator device operable to receive input data from the biometric system via a communication interface and to aggregate a batch of at least some of the received input data. The machine-learning system further comprises a learning expert device operable to compute a new suggestion for a matching threshold value of the biometric system based on the aggregated batch. Finally, the machine-learning system comprises an output device operable to output the computed new suggestion for the matching threshold of the biometric system via the communication interface. | 03-03-2016 |
20160063398 | SYSTEM AND METHOD FOR PROFILING REQUESTS IN SERVICE SYSTEMS - A system and method for profiling a request in a service system with kernel events including a pre-processing module configured to obtain kernel event traces from the service system and determine starting and ending communication pairs of a request path for a request. A learning module is configured to learn pairwise relationships between the starting and ending communication pairs of training traces of sequential requests. A generation module is configured to generate communication paths for the request path from the starting and ending communication pairs of testing traces of concurrent requests using a heuristic procedure that is guided by the learned pairwise relationships and generate the request path for the request from the communication paths. The system and method precisely determine request paths for applications in a distributed system from kernel event traces even when there are numerous concurrent requests. | 03-03-2016 |
20160070871 | SYSTEM AND METHOD FOR RECOMMENDING OPTIMUM INSULIN BOLUS DOSAGE - A computer-implemented method for recommending an optimum insulin bolus dosage to a patient is described. The computer-implemented method includes receiving diabetes related data from the patient. The computer implemented method further includes determining a plurality of insulin bolus dosages using a plurality of insulin bolus calculators. The plurality of insulin bolus dosages are calculated based on the diabetes related data. Thereafter, an optimum insulin bolus dosage is determined based on the plurality of insulin bolus dosages. The optimum insulin bolus dosage is then presented to the patient. | 03-10-2016 |
20160071011 | STREAM PROCESSING WITH DYNAMIC EVENT ROUTING - Methods, systems, and apparatus, including computer programs encoded on computer storage media, for routing events of an event stream. One of the methods includes operations of receiving, by a router, events of an event stream; providing each event, by the router, to a respective local modeler selected by the router according to an initial routing strategy, the respective local modeler being selected from multiple local modelers; aggregating, by each local modeler in parallel, information associated with each event received by the local modeler to generate aggregated information; providing, to a central modeler, the aggregated information generated by the one or more local modelers; determining, by the central modeler, parameters of a machine learning model using the aggregated information received by the central modeler and generating an updated routing strategy based on the parameters of the machine learning model; and providing the updated routing strategy to the router. | 03-10-2016 |
20160071014 | STREAM PROCESSING WITH MULTIPLE CONNECTIONS BETWEEN LOCAL AND CENTRAL MODELERS - Methods, systems, and apparatus, including computer programs encoded on computer storage media, for stream processing. One method includes receiving an event stream of first events by a first plurality of first local modelers of a stream processing system. Each local modeler processes a portion of received events of the event stream according to a first set of operations, the operations including aggregating information associated with each event to generate first aggregated information. A second plurality of second local modelers similarly generates second aggregated information from an event stream of second events. First and second local modelers provide, to a first central modeler, first and second aggregated information. A set of parameters of a respective machine learning model is determined by the first central modeler using the received aggregated information. | 03-10-2016 |
20160071022 | Machine Learning Model for Level-Based Categorization of Natural Language Parameters - A mechanism is provided in a data processing system for categorizing a user providing a text input. The mechanism receives an input text written by a user and determines a set of features associated with the input text. The mechanism processes the input text and the set of features by a detection model. The detection model comprises a plurality of detectors corresponding to a plurality of categories. Each of the plurality of detectors determines whether the user fits a respective category based on the input text and the set of features. The mechanism categorizes the user into one or more of the plurality of categories based on a result of processing the input text and the set of features by the detection model. | 03-10-2016 |
20160071023 | Computing Instance Launch Time - A technology is described for predicting a launch time for a computing instance. An example method may include receiving a request for a predicted launch time to launch a computing instance on a physical host within a computing service environment. Data associated with launch features of a computing instance may then be obtained, where the launch features may be determined to have an impact on a launch time of the computing instance on a physical host within a computing service environment. The launch features of the computing instance may then be input to a machine learning model that outputs the predicted launch time for launching the computing instance within the computing service environment. | 03-10-2016 |
20160071024 | DYNAMIC HYBRID MODELS FOR MULTIMODAL ANALYSIS - Technologies for analyzing temporal components of multimodal data to detect short-term multimodal events, determine relationships between short-term multimodal events, and recognize long-term multimodal events, using a deep learning architecture, are disclosed. | 03-10-2016 |
20160071025 | COMPUTE INTENSIVE STREAM PROCESSING - Methods, systems, and apparatus, including computer programs encoded on computer storage media, for stream processing. One method includes receiving an event stream of events by a first plurality of local modelers of a stream processing system. Each local modeler processes a portion of received events of the event stream according to a first set of operations, the operations including aggregating information associated with each event to generate aggregated information. One or more local modelers provide, to a first central modeler executing on the system, the respective aggregated information generated by one or more of the local modelers. A set of parameters of a respective machine learning model is determined using the received aggregated information. | 03-10-2016 |
20160071026 | COMPUTE INTENSIVE STREAM PROCESSING WITH CONTEXT DATA ROUTING - Methods, systems, and apparatus, including computer programs encoded on computer storage media, for routing events of an event stream in a stream processing system. One of the methods includes receiving, by a router, an event stream of events; identifying, for each event, by the router, a respective partition of context data that includes context data related to the event and providing the event to a respective local modeler that stores the partition of context data identified for the event in operational memory of the local modeler; processing, by each local modeler, events received from the router and aggregating information associated with each event to generate aggregated information; providing, by one or more of the local modelers, to a central modeler, the respective aggregated information; and determining, by the central modeler, a plurality of parameters of a machine learning model using the received aggregated information. | 03-10-2016 |
20160071027 | COMPUTE INTENSIVE STREAM PROCESSING WITH CONCEPT DRIFT DETECTION - Methods, systems, and apparatus, including computer programs encoded on computer storage media, for detecting trends in event streams. One method includes generating a first set of parameters of a machine learning model from a first system processing an event stream, the first system comprising a first central modeler that receives aggregated information from a first plurality of local modelers; generating a second set of parameters of the machine learning model from a second system processing the event stream, the second system comprising a second central modeler that receives aggregated information from a second plurality of local modelers; determining a difference between the first set of parameters and the second set of parameters; and determining that the difference is greater than a threshold amount and as a consequence outputting information identifying a trend in the event stream. | 03-10-2016 |
20160078022 | CLASSIFICATION SYSTEM WITH METHODOLOGY FOR EFFICIENT VERIFICATION - Techniques for a classification system with methodology for enhanced verification are described. In one approach, a classification computer trains a classifier based on a set of training documents. After training is complete, the classification computer iterates over a collection unlabeled documents uses the trained classifier to predict a label for each unlabeled document. A verification computer retrieves one of the documents assigned a label by the classification computer. The verification computer then generates a user interface that displays select information from the document and provides an option to verify the label predicted by the classification computer or provide an alternative label. The document and the verified label are then fed back into the set of training documents and are used to retrain the classifier to improve subsequent classifications. In addition, the document is indexed by a query computer based on the verified label and made available for search and display. | 03-17-2016 |
20160078184 | MACHINE LEARNING FOR HEPATITIS C - To predict which Hepatitis C patients are at high-risk for disease progression or adverse health outcomes, baseline characteristics are measured for patients as well as longitudinal data, including clinical, laboratory and/or biopsy results, which may be collected periodically in follow-up visits with a healthcare professional. A machine learning engine may predict whether a patient is at high-risk for disease progression or adverse health outcomes based on the baseline characteristics and the longitudinal data for the patient. | 03-17-2016 |
20160078340 | Machine Operation Classifier - A process for developing machine classification systems includes using human experts to associate expected operations with various machine states including drawbar pull, tool position, tool commands, gear, and ground speed, among others, to create a classification system that can be used in a particular machine. The classification system operates in real time to infer operations such as dig, dump, travel, and push from machine state inputs and logs the operations for use in operational analysis and maintenance of the machine. | 03-17-2016 |
20160078347 | Methods and Systems for Aggregated Multi-Application Behavioral Analysis of Mobile Device Behaviors - A computing device processor may be configured with processor-executable instructions to implement methods of using behavioral analysis and machine learning techniques to evaluate the collective behavior of two or more software applications operating on the device. The processor may be configured to monitor the activities of a plurality of software applications operating on the device, collect behavior information for each monitored activity, generate a behavior vector based on the collected behavior information, apply the generated behavior vector to a classifier model to generate analysis information, and use the analysis information to classify a collective behavior of the plurality of software applications. | 03-17-2016 |
20160078348 | Automatic Case Assignment Based on Learned Expertise of Prior Caseload - A mechanism is provided in a data processing system for automatic case assignment. The mechanism extracts features from a machine readable form of a case to be assigned. An expertise classifier generates an initial case assignment matrix matching the case to a plurality of caseworkers based on the extracted features, a caseworker relationship graph, and an entity relationship graph. A personnel filter filters the initial case assignment matrix based on expertise, availability, and caseload of the plurality of caseworkers to form a final caseworker assignment. The mechanism assigns the case to an identified caseworker based on the final caseworker assignment. | 03-17-2016 |
20160078349 | Method for Identifying Verifiable Statements in Text - A method, system and computer-usable medium are disclosed for identifying verifiable statements in a corpus of text. A training corpus of text containing manually annotated instances of verifiable and non-verifiable statements is processed to parse the text into segmented statements, which are in turn processed to extract features. The extracted features and the annotated statements are then processed with a machine learning algorithm to generate a verifiable statement classification model. In turn, the verifiable statement classification model is referenced by a verifiable statement classification system to distinguish verifiable and non-verifiable statements contained within an input corpus of text. | 03-17-2016 |
20160078353 | MONITORING USER STATUS BY COMPARING PUBLIC AND PRIVATE ACTIVITIES - One embodiment of the present invention provides a system for detecting anomalous correlations between public and private activities of a user. During operation, the system collects public and private activity data associated with the user. The system generates a series of feature pairs, each feature pair including a public feature vector and a private feature vector generated from the activity data. Each respective feature pair corresponds to a respective point in time. The system generates a model to determine whether there is an anomaly in a correlation between the user's public and private activity data. The model is associated with a normal correlation between the user's public and private activity data over a period of time. The system collects additional public and private activity data and applies the model to determine whether there is an anomaly. The system may issue an alert in response to detecting an anomaly. | 03-17-2016 |
20160078359 | SYSTEM FOR DOMAIN ADAPTATION WITH A DOMAIN-SPECIFIC CLASS MEANS CLASSIFIER - A classification system includes memory which stores, for each of a set of classes, a classifier model for assigning a class probability to a test sample from a target domain. The classifier model has been learned with training samples from the target domain and from at least one source domain. Each classifier model models the respective class as a mixture of components, the component mixture including a component for each source domain and a component for the target domain. Each component is a function of a distance between the test sample and a domain-specific class representation which is derived from the training samples of the respective domain that are labeled with the class, each of the components in the mixture being weighted by a respective mixture weight. Instructions, implemented by a processor, are provided for labeling the test sample based on the class probabilities assigned by the classifier models. | 03-17-2016 |
20160078361 | OPTIMIZED TRAINING OF LINEAR MACHINE LEARNING MODELS - An indication of a data source to be used to train a linear prediction model is obtained. The model is to generate predictions using respective parameters assigned to a plurality of features derived from observation records of the data source. The parameter values are stored in a parameter vector. During a particular learning iteration of the training phase of the model, one or more features for which parameters are to be added to the parameter vector are identified. In response to a triggering condition, parameters for one or more features are removed from the parameter vector based on an analysis of relative contributions of the features represented in the parameter vector to the model's predictions. After the parameters are removed, at least one parameter is added to the parameter vector. | 03-17-2016 |
20160078362 | Methods and Systems of Dynamically Determining Feature Sets for the Efficient Classification of Mobile Device Behaviors - Methods and devices for detecting suspicious or performance-degrading mobile device behaviors may include monitoring the activities of the software application by collecting behavior information, generating a behavior vector that includes a behavior feature that identifies an aspect of a monitored activity of the software application, applying the generated behavior vector to a classifier model to generate analysis results, using the analysis results to update the behavior feature so that it identifies a different aspect of the monitored activity, regenerating the behavior vector to include the updated behavior feature, and applying the regenerated behavior vector to the classifier model to determine whether the software application is non-benign. | 03-17-2016 |
20160078363 | Method for Developing Machine Operation Classifier Using Machine Learning - A method for developing machine operation classifiers for a machine is disclosed. The method includes receiving training data associated with the machine from one or more on-board engineering channels associated with the machine and determining one or more training features based on the training data values. The method also includes determining one or more training labels associated with the one or more training features and building a predictive model for determining machine operation classifiers using a computer. Building the predictive model may include feeding the one or more training features and the one or more training labels associated with the one or more training features to a machine learning algorithm and determining a predictive model from the machine learning algorithm. The predictive model may be used for receiving new data associated with the machine and determining a predicted label based on the new data. | 03-17-2016 |
20160078365 | AUTONOMOUS DETECTION OF INCONGRUOUS BEHAVIORS - Behavioral characteristics of at least a first machine component are monitored. A model that represents machine-to-machine interactions between at least the first machine component and at least a further machine component is generated. Using the monitored behavioral characteristics and the generated model, an incongruity of a behavior of at least the first machine component and the machine-to-machine interactions is computed, where the incongruity is predicted based on determining a discordance between an expectation of the system and the behavior and the machine-to-machine interactions, and wherein the predicting is performed without using a previously built normative rule of behavior and machine-to-machine interactions. | 03-17-2016 |
20160078366 | Computer system of an artificial intelligence of a cyborg or an android, wherein a received signal-reaction of the computer system of the artificial intelligence of the cyborg or the android, a corresponding association of the computer system of the artificial intelligence of the cyborg or the android, a corresponding thought of the computer system of the artificial intelligence of the cyborg or the android are physically built, and a working method of the computer system of the artificial intelligence of the artificial intelligence of the cyborg or the android - A computer system of an artificial intelligence of a cyborg or an android, wherein a received signal-reaction of the computer system of the artificial intelligence of the cyborg or the android, a corresponding association of the computer system of the artificial intelligence of the cyborg or the android, a corresponding thought of the computer system of the artificial intelligence of the cyborg or the android are physically built, and a working method of the computer system. The computer system is based on one natural language. The computer system comprises at least five senses equipped with sense organs, wherein the senses are a sense of sight, a sense of hearing, a sense of smell, a sense of taste, a sense of touch. The sensors network summarizes all reactions of all sensors of all sensor groups of all sense organs of all senses. | 03-17-2016 |
20160078367 | DATA CLEAN-UP METHOD FOR IMPROVING PREDICTIVE MODEL TRAINING - A method that improves the training of predictive models. Better trained predictive models make better predictions, and can classify transactions with reduced levels of false positives and false negative. Included is an apparatus for executing a data clean-up algorithm that harmonizes a wide range of real world supervised and unsupervised training data into a single, error-free, uniformly formatted record file that has every field coherent and well populated with information. | 03-17-2016 |
20160078368 | ARTIFICIAL INTELLIGENCE & KNOWLEDGE BASED AUTOMATION ENHANCEMENT - This invention generally relates to a process, system and computer code for updating of computer applications based on collecting automation information related to a current application such as processing power, load, footprint, and performance attributes, determining a system automation profile; using an artificial intelligence based modeler for analyzing data, applying the data to an artificial intelligence model for training and predicting performance, adjusting the artificial intelligence model to achieve an updated automation criteria with optimal values, wherein the optimal values provide input to an automation criteria library for storing and updating a prior automation criteria, and exporting the upgraded automation criteria values for incorporation in a computer-to-be-updated, to achieve a reliable automatic update. | 03-17-2016 |
20160078369 | Methods for training emotional response predictors utilizing attention in visual objects - Described herein are methods for training a predictor of a user's emotional response to stimuli (e.g., digital content). In order to more accurately learn the nature of the emotional response of the user to the stimuli, in some embodiments, the training of the predictor involves collection of attention level data that indicates to which objects the user paid attention. The attention level data may be utilized to weight token instances representing visual objects from the stimuli. Such a weighting can help to train the emotional response predictor to better determine which objects influence the user's affective response and/or the extent of their influence on the user's affective response. In different embodiments, attention level information may come from different sources, such as eye tracking data of the user, and a model for predicting an interest level of the user in various visual objects. | 03-17-2016 |
20160080523 | ENHANCED FEATURE VECTOR - The number, popularity, sophistication, etc. of mobile applications have grown dramatically with the rise of smartphones, tablets, and other such devices. Alternatives to native application development, including approaches such as hybrid application development which may employ among other things a container paradigm, inter alia address various of the drawbacks associated with native application development. A flexible, extensible, and dynamically configurable Feature Vector (FV) facility addresses one challenge with approaches such as hybrid application development—controlling an application's access to features (e.g., functions, methods, resources, etc.) and the efficient administration, management, etc. same. | 03-17-2016 |
20160086079 | PROPERTIES LINK FOR SIMULTANEOUS JOINT INVERSION - A method can include receiving data associated with a geologic environment; based on at least a portion of the data, estimating relationships for multiple properties of the geologic environment; and based at least in part on the relationships, performing simultaneous joint inversion for at least one property of the geologic environment. | 03-24-2016 |
20160086087 | METHOD FOR FAST PREDICTION OF GAS COMPOSITION - A method and device for predicting a gas composition, including pre-processing, by non-negative matrix factorization, a set of input parameters related to a fluid mixture of hydrocarbons and non-hydrocarbons fed into a multistage separator, and training an extreme learning machine model to predict the composition of non-hydrocarbons in the fluid mixture. | 03-24-2016 |
20160086097 | Automatic Discovery of Message Ordering Invariants in Heterogeneous Logs - A method and system are provided. The method includes performing, by a logs-to-time-series converter, a logs-to-time-series conversion by transforming a plurality of heterogeneous logs into a set of time series. Each of the heterogeneous logs includes a time stamp and text portion with one or more fields. The method further includes performing, by a time-series-to-sequential-pattern converter, a time-series-to-sequential-pattern conversion by mining invariant relationships between the set of time series, and discovering sequential message patterns and association rules in the plurality of heterogeneous logs using the invariant relationships. The method also includes executing, by a processor, a set of log management applications, based on the sequential message patterns and the association rules. | 03-24-2016 |
20160086098 | Temporal Memory Using Sparse Distributed Representation - A processing node in a temporal memory system includes a spatial pooler and a sequence processor. The spatial pooler generates a spatial pooler signal representing similarity between received spatial patterns in an input signal and stored co-occurrence patterns. The spatial pooler signal is represented by a combination of elements that are active or inactive. Each co-occurrence pattern is mapped to different subsets of elements of an input signal. The spatial pooler signal is fed to a sequence processor receiving and processed to learn, recognize and predict temporal sequences in the input signal. The sequence processor includes one or more columns, each column including one or more cells. A subset of columns may be selected by the spatial pooler signal, causing one or more cells in these columns to activate. | 03-24-2016 |
20160086099 | SELECTING STRANGERS FOR INFORMATION SPREADING ON A SOCIAL NETWORK - A computer-implemented method, computer program product, and computer system for selecting strangers for information spreading on a social network. For the strangers who are users of the social network and not related to each other, information spreading probabilities based on features, information reach, and information spreading probabilities based on a wait time are computed. Fitness scores of the strangers are computed; the fitness scores are a function of the information spreading probabilities, the information reach, and the information spreading probabilities. The strangers are ranked, based on the fitness scores, in a sorted set. One or more of the strangers for the information spreading are selected from the sorted set. The one or more of the strangers for the information spreading are selected by determining an interval in the sorted set, and the interval satisfies an optimization objective of maximizing an information spreading rate. | 03-24-2016 |
20160086100 | SELECTING STRANGERS FOR INFORMATION SPREADING ON A SOCIAL NETWORK - A computer-implemented method, computer program product, and computer system for selecting strangers for information spreading on a social network. For the strangers who are users of the social network and not related to each other, information spreading probabilities based on features, information reach, and information spreading probabilities based on a wait time are computed. Fitness scores of the strangers are computed; the fitness scores are a function of the information spreading probabilities, the information reach, and the information spreading probabilities. The strangers are ranked, based on the fitness scores, in a sorted set. One or more of the strangers for the information spreading are selected from the sorted set. The one or more of the strangers for the information spreading are selected by determining an interval in the sorted set, and the interval satisfies an optimization objective of maximizing unit information reach per stranger. | 03-24-2016 |
20160092380 | LEVELING IO - A method, system, and computer program product for IO leveling comprising receiving an IO, determining if there is a delay for processing IO because of pending IO, based on a positive determination there is a delay for processing IO, determining a priority for the IO, and based on the priority of IO determining whether to process the IO. | 03-31-2016 |
20160092465 | SHARED FILE SYSTEM PREDICTIVE STORAGE TECHNIQUES - Disclosed in some examples are predictive storage techniques for use in a distributed data system. The predictive storage techniques may be used to manage locally stored elements of a shared data collection, such as the storage of files on nodes of the distributed data system that are limited in local storage space. The predictive storage techniques may achieve a balance between consumption of local resources and timely access of important elements in the shared data collection. For example, the predictive storage techniques may be used for keeping or pre-caching certain items of a collection that are determined as likely to be used in local storage for convenient access, and allowing access the remaining items on request over a network. | 03-31-2016 |
20160092772 | SYSTEM AND METHOD FOR CALCULATING SEARCH TERM PROBABILITY - A system and method for predicting search term popularity is disclosed herein. A database system may comprise a first database cluster H and a second database cluster L. A machine learning algorithm is trained to create a predictive model. Thereafter, for each record in a database system, the predictive model is used to calculate a probability of the record being accessed. If the calculated probability of the record being accessed is greater than a threshold value, then the record in the first database cluster H; otherwise, the record is placed in the second database cluster L. Training the machine learning algorithm comprises inputting a training feature vector associated with the record into the machine learning algorithm, inputting a cost vector into the machine learning algorithm, and iteratively operating the machine learning algorithm on each record in the set of records to create a predictive model. Other embodiments are also disclosed herein. | 03-31-2016 |
20160092774 | DETERMINING AND LOCALIZING ANOMALOUS NETWORK BEHAVIOR - Methods, systems, and apparatus, including computer programs encoded on computer storage media, for detecting and localizing anomalies in large data sets. One of the methods includes identifying a user whose behavior is classified as anomalous during a particular time interval and determining observed community feature values for a community of users of which the user is a member. If observed user feature values are consistent with the observed community feature values, the behavior of the user is classified as not anomalous. If the observed user feature values are not consistent with the observed community feature values, the behavior of the user is classified as anomalous. | 03-31-2016 |
20160092782 | INFORMATION PROCESSING APPARATUS, PROGRAM AND INFORMATION PROCESSING METHOD - To identify a scenario that will bear a good simulation result from among a plurality of scenarios used in an agent-based simulation with a reduced amount of computation, there is provided an information processing apparatus comprising a counting part configured to count the number of agents in each of a plurality of states at a middle of a simulation that involves a plurality of agents, and a generation part configured to generate characteristic data used for prediction of a result of the simulation based on the number of agents in each of the plurality of states. | 03-31-2016 |
20160092784 | CHARACTERIZATION OF GRAPHICAL REPRESENTATION OF NUMERICAL SIMULATION RESULTS - Methods of characterizing or classifying graphical representation of numerical simulation results are disclosed. A training database is created in a computer system by including a plurality of graphical representations of respective results obtained from a plurality of numerical simulations. Each graphical representation is associated with a textual description of a pertinent feature related to the numerical simulations by user. A quality index with respect to the associated textual description is calculated for each graphical representation by application module using an autocorrelation technique of correlating all graphical representations with one another in the training database. A new graphical representation obtained from another numerical simulation can then be characterized with one of the textual descriptions and a corresponding confidence score by comparing the new graphical representation with all graphical representations in the training database. The training database may be improved by adding or removing appropriate graphical representations in accordance with predefined criteria. | 03-31-2016 |
20160092787 | BEHAVIORAL MODELING OF A DATA CENTER UTILIZING HUMAN KNOWLEDGE TO ENHANCE A MACHINE LEARNING ALGORITHM - A method generates a behavioral model of a data center when a machine learning algorithm is applied. A team of human modelers that partition the data center into a plurality of connected nodes is analyzed by a behavioral model. The behavioral model of the data center detects an anomaly in a system behavior center by recursively applying the behavioral model to each node and simple component. A compressed metric vector for the node is generated by reducing a dimension of an input metric vector. A root cause of a failure caused is determined by the anomaly and an action is automatically recommended to an operator to resolve a problem caused by the failure. The proactively actions are taken to keep the data center in a normal state based on the behavioral model using the machine learning algorithm. | 03-31-2016 |
20160092788 | Learning System - Various embodiments are described that relate to an adaptive learning system. The adaptive learning system can be trained by correlation between a first set of raw technical performance data and a set of actual operational effectiveness assessment data. Once trained, the adaptive learning system can be deployed. Once deployed, the adaptive learning system can produce a set of predicted operational effectiveness assessment data from a second set of raw technical performance data that is different from the first set of raw technical performance data. | 03-31-2016 |
20160092789 | Category Oversampling for Imbalanced Machine Learning - Methods, systems, and computer program products for category oversampling for imbalanced machine learning are provided herein. A method includes identifying an anchor data point in a given class of data points underrepresented among multiple classes in a data set of multiple data points, wherein each data point represent a vector; determining a number of data points in the given class that neighbor the anchor data point, wherein the number comprises two or more; applying a weight to (i) each of the number of data points to create a number of weighted neighboring data points, and (ii) the anchor data point to create a weighted anchor data point, wherein the sum of all weights is equal to one; performing a vector summation by summing the number of weighted neighboring data points and the weighted anchor data point; and generating a synthetic data point based on said vector summation. | 03-31-2016 |
20160092790 | METHOD FOR MULTICLASS CLASSIFICATION IN OPEN-SET SCENARIOS AND USES THEREOF - The proposed method is used for classification in open-set scenarios, wherein often it is not possible to first obtain the training data for all possible classes that may arise during the testing stage. During the test phase, test samples belonging to one of the classes used in the training phase are classified based on a ratio between similarity scores, as known correct class and test samples belonging to any other class are to be rejected and classified as unknown. | 03-31-2016 |
20160092791 | MINING TEXTUAL FEEDBACK - Methods, systems, and apparatus for mining feedback are described. A set of one or more lexical patterns associated with one or more of a suggestion and a defect report are determined and the set of one or more lexical patterns are matched against a plurality of feedback items to generate a distance learning training set. A distance learning technique is applied to the distance learning training set to generate a distance learning model and the distance learning model is used to identify one or more candidate feedback items of the plurality of feedback items, each of which is one or more of a candidate suggestion and a candidate defect report. | 03-31-2016 |
20160092792 | Method for Dynamically Assigning Question Priority Based on Question Extraction and Domain Dictionary - An approach is provided dynamically prioritizing question requests based on extracted question data. In the approach, performed by an information handling system, a number of question requests to a question and answering (QA) system are received from a computer network, and a plurality of question priority parameters are identified, including one or more question topics and a plurality question context parameters, by performing natural language processing (NLP) analysis of each question request. The approach determines a target priority value for each question request based on the plurality of question priority parameters identified for said question request. By evaluating the target priority values for the plurality of question requests, processing of the question requests is prioritized, such as by applying an artificial intelligence (AI) learned models and rule-based logic at the information handling system to evaluate the target priority values for the plurality of question requests. | 03-31-2016 |
20160092793 | PHARMACOVIGILANCE SYSTEMS AND METHODS UTILIZING CASCADING FILTERS AND MACHINE LEARNING MODELS TO CLASSIFY AND DISCERN PHARMACEUTICAL TRENDS FROM SOCIAL MEDIA POSTS - Systems and methods for utilizing filters to reduce an incoming stream of textual messages to a smaller subset of potentially relevant textual messages, and using trained machine learning models to analyze and classify the content of such textual messages. Analyzed messages that belong to a relevant class as determined by the machine learning model are stored in a database, giving users the ability to determine and analyze trends from the subset of messages, such as adverse side effects caused by pharmaceuticals or the efficacy of pharmaceuticals. Relationships between the side effects caused by different pharmaceuticals can be used to predict potential candidates for drug repositioning. | 03-31-2016 |
20160092794 | GENERAL FRAMEWORK FOR CROSS-VALIDATION OF MACHINE LEARNING ALGORITHMS USING SQL ON DISTRIBUTED SYSTEMS - A general framework for cross-validation of any supervised learning algorithm on a distributed database comprises a multi-layer software architecture that implements training, prediction and metric functions in a C++ layer and iterates processing of different subsets of a data set with a plurality of different models in a Python layer. The best model is determined to be the one with the smallest average prediction error across all database segments. | 03-31-2016 |
20160098631 | APPARATUS AND METHOD FOR LEARNING A MODEL CORRESPONDING TO TIME-SERIES INPUT DATA - A dynamic time-evolution Boltzmann machine capable of learning is provided. Aspects include acquiring a time-series input data and supplying a plurality of input values of input data of the time-series input data at one time point to a plurality of nodes of the mode. Aspects also include computing, based on an input data sequence before the one time point in the time-series input data and a weight parameter between each of a plurality of input values of input data of the input data sequence and a corresponding one of the plurality of nodes of the model, a conditional probability of the input value at the one time point given that the input data sequence has occurred. Aspects further include adjusting the weight parameter so as to increase a conditional probability of occurrence of the input data at the one time point given that the input data sequence has occurred. | 04-07-2016 |
20160098636 | DATA PROCESSING APPARATUS, DATA PROCESSING METHOD, AND RECORDING MEDIUM THAT STORES COMPUTER PROGRAM - Provided is a data processing apparatus creates teacher data used for learning of a machine learning system by classifying data extracted from time series data on the basis of a specific reference. The data processing apparatus includes a data extraction unit which extracts a candidate of teacher data, from time series data, a teacher data creation unit which creates the teacher data based on a label to classify the candidate of teacher data, and the candidate of teacher data which are labeled, and a teacher data complement unit which further extracts the candidate of the teacher data from the time series data that exists between a specific candidate of the teacher data at a specific timing and one of other candidates of the teacher data at a timing different from the specific timing, based on a degree of a variation between these candidates of the teacher data. | 04-07-2016 |
20160098637 | Automated Data Analytics for Work Machines - Systems and methods for aggregating classifications of predictive operations of a machine may include one or more sensors in communication with a processor. Such systems and methods may be implemented to receive on-board machine time series data, process the data, segment the processed data, resolve the segmented data, and characterize the segmented data. Additionally, such systems and methods may be implemented to determine profile percentage breakdowns of a machine, performance reports, and productivity reports, and inform composite work cycles. | 04-07-2016 |
20160098639 | METHOD AND APPARATUS FOR ESTIMATING POWER CONSUMPTION BASED ON TEMPERATURE - Accordingly the embodiments herein achieve a method and apparatus for estimating power consumption based on a temperature. The method includes dividing a use time and a non-use time based on an analysis of a correlation of a determined temperature and a power consumption based on the determined temperature. Further, the method includes extracting a base consumption of an electrical apparatus based on power consumption during the non-use time. Further, the method includes extracting a threshold temperature and a power consumption continuous function based on the threshold temperature and power consumption. The threshold temperature requires a maximum operation of the electrical apparatus. Furthermore, the method includes calculating the power consumption based on a predetermined temperature from the power consumption continuous function; and calculating an estimated power consumption except for the base consumption. | 04-07-2016 |
20160098642 | DETRMINING VARIABLE OPTIMAL POLICY FOR AN MDP PARADIGM - A method for determining a variable near-optimal policy for a problem formulated as Markov Decision Process, the problem comprising at least one limited action entry, the limited action entry being an entry of an action of a finite set of actions limited in the number of times its value may be changed, the method comprising using at least one hardware processor for: receiving data elements with respect to the problem, the data elements comprising: (a) a finite set of states, (b) the finite set of actions, (c) a transition probabilities matrix determining transition probabilities between states of the finite set of states, once actions of the set of actions are performed; (d) an immediate cost function, wherein the value of the immediate cost function is determined for a pair of a state of the finite set of states and an action of the finite set of actions, and (e) a discount factor; updating one or more data elements of the received data elements relating to the at least one limited action entry, wherein the one or more data elements are selected from the group consisting of: the transition probabilities matrix, the immediate cost function and the discount factor, and wherein the updating is triggered by a change of a value of a limited action entry of the at least one limited action entry; and following the updating of the one or more data elements, calculating a current near-optimal policy for the problem based on the updated one or more data elements. | 04-07-2016 |
20160098645 | HIGH-PRECISION LIMITED SUPERVISION RELATIONSHIP EXTRACTOR - Automatic relationship extraction is provided. A machine learning approach using statistical entity-type prediction and relationship predication models built from large unlabeled datasets is interactively combined with minimal human intervention and a light pattern-based approach to extract relationships from unstructured, semi-structured, and structured documents. Training data is collected from a collection of unlabeled documents by matching ground truths for a known entity from existing fact databases with text in the documents describing the known entity and corresponding models are built for one or more relationship types. For a modeled relationship-type, text chunks of interest are found in a document. A machine learning classifier predicts the probability that one of the text chunks is the entity being sought. The combined machine learning and light pattern-based approach provides both improved recall and high precision through filtering and allows constraining and normalization of the extracted relationships. | 04-07-2016 |
20160098646 | DYNAMICALLY MODIFYING A BOUNDARY OF A DEEP LEARNING NETWORK - A connection between a user device and a network server is established. Via the connection, a deep learning network is formed for a processing task. A first portion of the deep learning network operates on the user device and a second portion of the deep learning network operates on the network server. Based on cooperation between the user device and the network server, a boundary between the first portion and the second portion of the deep learning network is dynamically modified based on a change in a performance indicator that could affect the processing task | 04-07-2016 |
20160098648 | PREDICTING A CONSUMER SELECTION PREFERENCE BASED ON ESTIMATED PREFERENCE AND ENVIRONMENTAL DEPENDENCE - An information processing apparatus includes a history acquisition section configured to acquire history data including a history indicating that a plurality of selection subjects have selected selection objects; a learning processing section configured to allow a choice model to learn a preference of each selection subject for a feature and an environmental dependence of selection of each selection object in each selection environment using the history data, where the choice model uses a feature value possessed by each selection object, the preference of each selection subject for the feature, and the environmental dependence indicative of ease of selection of each selection object in each of a plurality of selection environments to calculate a selectability with which each of the plurality of selection subjects selects each selection object; and an output section configured to output results of learning by the learning processing section. | 04-07-2016 |
20160104070 | INFERENCE ENGINE FOR EFFICIENT MACHINE LEARNING - An inference engine is described for efficient machine learning. For example, an inference engine executes a plurality of ordered steps to carry out inference on the basis of observed data. For each step, a plurality of inputs to the step are received. A predictor predicts an output of the step and computes uncertainty of the prediction. Either the predicted output or a known output is selected on the basis of the uncertainty. If the known output is selected, the known output is computed, (for example, using a resource intensive, accurate process). The predictor is retrained using the known output and the plurality of inputs of the step as training data. For example, computing the prediction is fast and efficient as compared with computing the known output. | 04-14-2016 |
20160104074 | Recommending Bidded Terms - Systems and methods for recommending bidded terms are disclosed. The system collects a plurality of bidded terms and separates them into ad groups. The add groups are then combined into sequences of terms, which are fed into a deep learning network to build a multidimensional word vector in which related terms are nearer one another than unrelated terms. An input term is then received and the system matches the input term in the multidimensional word vector and recommends the nearest neighbors to the term. | 04-14-2016 |
20160104075 | IDENTIFYING SALIENT TERMS FOR PASSAGE JUSTIFICATION IN A QUESTION ANSWERING SYSTEM - According to an aspect, a term saliency model is trained to identify salient terms that provide supporting evidence of a candidate answer in a question answering computer system based on a training dataset. The question answering computer system can perform term saliency weighting of a candidate passage to identify one or more salient terms and term weights in the candidate passage based on the term saliency model. The one or more salient terms and term weights can be provided to at least one passage scorer of the question answering computer system to determine whether the candidate passage is justified as providing supporting evidence of the candidate answer. | 04-14-2016 |
20160104076 | ADAPTIVE KEY PERFORMANCE INDICATOR THRESHOLDS - Techniques are disclosed for providing adaptive thresholding technology for Key Performance Indicators (KPIs). Adaptive thresholding technology may automatically assign new values or adjust existing values for one or more thresholds of one or more time policies. Assigning threshold values using adaptive thresholding may involve identifying training data (e.g., historical data, simulated data, or example data) for the time frames and analyzing the training data to identify variations within the data (e.g., patterns, distributions, trends). A threshold value may be determined based on the variations and may be assigned to one or more of the thresholds without additional user intervention. | 04-14-2016 |
20160104077 | System and Method for Extracting Table Data from Text Documents Using Machine Learning - Systems and methods for extracting table data from text documents using machine learning are provided. The systems and methods comprise electronically receiving at a computer system a document having one or more tables, each table having one or more whitespace features, processing the document using a first computer model executed by the computer system to classify each row of the one or more tables as a header row or a data row, processing the document using a second computer model executed by the computer system to classify each whitespace feature in each row conditional on classification of each row by the first computer model, the second computer model identifying whether a whitespace feature corresponds to information missing from the one or more tables, and generating an output of the classified whitespace features and storing the output in a digital file. | 04-14-2016 |
20160110422 | QUERY RESPONSE DEVICE - The invention concerns a query response device comprising: an input adapted to receive user queries; a memory ( | 04-21-2016 |
20160110448 | Dynamic Load Balancing Based on Question Difficulty - Mechanisms are provided for performing load balancing of question processing in a Question and Answer (QA) system, implemented by the data processing system, having a plurality of QA system pipelines. The mechanisms receive an input question for processing by the QA system and determine a predicted question difficulty of the input question. The mechanisms select a QA system pipeline from the plurality of QA system pipelines based on the predicted question difficulty and route the input question to the selected QA system pipeline for processing. In addition, the mechanisms process the input question with the selected QA system pipeline to generate an answer for the input question. | 04-21-2016 |
20160110653 | METHOD AND APPARATUS FOR PREDICTING A SERVICE CALL FOR DIGITAL PRINTING EQUIPMENT FROM A CUSTOMER - A method, non-transitory computer readable medium, and apparatus for predicting a service call for digital printing equipment from a customer are disclosed. For example, the method detects a triggering event based upon a number of detections of an event on a digital printing equipment exceeding a threshold within a predefined time period, wherein the number of detected events on the digital printing equipment exceeding the threshold within the predefined time period is indicative of an impending soft failure, calculates a probability that the customer will place the service call due to the impending soft failure within a second predefined period of time based on a fusion of a hazard model of the digital printing equipment, a customer behavior model and the number of detections of the event in response to the triggering event being detected and determines an action based upon the probability using a cost based utility function. | 04-21-2016 |
20160110655 | System of Sequential Kernel Regression Modeling for Forecasting and Prognostics - A monitoring system for determining the future operational condition of an object includes an empirical model to receive reference data that indicates the normal operational state of the object and input multi-dimensional pattern arrays. Each input pattern array has a plurality of input vectors, while each input vector represents a time point and has input values representing a plurality of parameters indicating the current condition of the object obtained from one or more first sensors at any time. The model generates estimate values that include at least one estimate vector of inferred estimate values for at least one future point in time or a plurality of second sensors being different from the one or more first sensors. The inferred estimate values are used to determine a current outcome of the object. | 04-21-2016 |
20160110656 | FEATURE SELECTION - A novel method and/or system of feature selection is described. | 04-21-2016 |
20160110657 | Configurable Machine Learning Method Selection and Parameter Optimization System and Method - A system and method for selecting a machine learning method and optimizing the parameters that control its behavior including receiving data; determining, using one or more processors, a first candidate machine learning method; tuning, using one or more processors, one or more parameters of the first candidate machine learning method; determining, using one or more processors, that the first candidate machine learning method and a first parameter configuration for the first candidate machine learning method are the best based on a measure of fitness subsequent to satisfaction of a stop condition; and outputting, using one or more processors, the first candidate machine learning method and the first parameter configuration for the first candidate machine learning method. | 04-21-2016 |
20160117400 | SYSTEM, METHOD AND APPARATUS FOR AUTOMATIC TOPIC RELEVANT CONTENT FILTERING FROM SOCIAL MEDIA TEXT STREAMS USING WEAK SUPERVISION - Presented are a system, method, and apparatus for automatic topic relevant content filtering from social media text streams using weak supervision. A computing device utilizes heuristic rules allowing topic filtering and a data stream data chunk identifier. A plurality of messages are transmitted as streaming message data from a social media network in real-time. The messages are split into a plurality of data stream data chunks according to the data stream data chunk identifier. A rule-based labeled data set L | 04-28-2016 |
20160117457 | METHOD AND APPARATUS FOR ANALYZING PATIENT'S CONSTITUTIONAL PECULIARITY - A method of analyzing checkup data of a target object, using an apparatus including at least one processor, includes receiving checkup data of a target object associated with a first disease, the checkup data including checkup values for a plurality of onset factors of the first disease; determining whether the checkup data corresponds to a first disease statistic model obtained from checkup values of a plurality of objects associated with the first disease; and calculating, when the checkup data is determined not to correspond to the first disease statistic model as a result of the determination, a peculiarity value of the target object such that a sum of adjusted checkup values, the adjusted checkup values being obtained by adjusting checkup values for respective onset factors of the first disease of the target object based on the peculiarity value, is equal to a reference value. | 04-28-2016 |
20160117589 | SYSTEM AND METHOD FOR AUTOMATIC DOCUMENT CLASSIFICATION IN EDISCOVERY, COMPLIANCE AND LEGACY INFORMATION CLEAN-UP - A system, method and computer program product for automatic document classification, including an extraction module configured to extract structural, syntactical and/or semantic information from a document and normalize the extracted information; a machine learning module configured to generate a model representation for automatic document classification based on feature vectors built from the normalized and extracted semantic information for supervised and/or unsupervised clustering or machine learning; and a classification module configured to select a non-classified document from a document collection, and via the extraction module extract normalized structural, syntactical and/or semantic information from the selected document, and generate via the machine learning module a model representation of the selected document based on feature vectors, and match the model representation of the selected document against the machine learning model representation to generate a document category, and/or classification for display to a user. | 04-28-2016 |
20160117592 | EFFECTIVE RESPONSE PROTOCOLS RELATING TO HUMAN IMPAIRMENT ARISING FROM INSIDIOUS HETEROGENEOUS INTERACTION - Structures and protocols are presented for using an identification of a first entity (an individual) or a second entity (a device or individual, e.g.), and an indication of the first entity not reacting positively (apparently taking offense, e.g.) to an action or expression of the second entity, for triggering one or more decision such as (1) whether or not to discard a recorded data component of a communicative expression of the second entity or (2) whether or not to facilitate a communication to a third entity or (3) whether or not to adjust a performance evaluation of the second party or of content from the second party or (4) whether or not to signal a disruptive emission in a vicinity of the second party. | 04-28-2016 |
20160117595 | INFORMATION RECOMMENDATION METHOD AND APPARATUS IN SOCIAL MEDIA - The present invention provides an information recommendation method and apparatus in a social media. An off-line procedure includes: determining a point of interest of a target user; selecting information related to the point of interest as an annotated corpus; and training an interest classification model of the target user by using the annotated corpus as a training sample. An on-line procedure includes: inputting to-be-recommended information to the interest classification model of the target user, so as to determine whether the to-be-recommended information tallies with an interest of the target user; and if the to-be-recommended information tallies with the interest of the target user, recommending the to-be-recommended information to the target user. According to the present invention, an effect of information recommendation in a social media can be improved. | 04-28-2016 |
20160117600 | Consistent Ordinal Reduced Error Logistic Regression Machine - An invention in the form of a Consistent Reduced Error Logistic Regression (RELR) Machine method is detailed. This invention includes mechanisms to result in logically consistent, explicit and more reliable learning within the RELR method related to ordinal target outcomes. | 04-28-2016 |
20160117603 | INTERACTIVE LEARNING - A system and method are provided for shared machine learning. The method includes providing a model to a plurality of agents included in a machine learning system. The model specifies attributes and attribute value data types for an event in which the agents act. The method further includes receiving agent-provided inputs during an instance of the event. The agent-provided inputs include estimated attribute values that are consistent with the attribute value data types. The method also includes determining expertise weights for at least some agents in response to at least one ground-truth which is learned from the estimated attribute values. The method additionally includes determining an estimate value for one or more of the attributes using respective adaptive mixtures of the estimated attribute values. | 04-28-2016 |
20160117604 | INFORMATION DISCOVERY SYSTEM - Systems, device and techniques are disclosed for an information discovery system. An element of data may be retrieved. A knowledge point may be extracted from the element of data. The knowledge point may include an aspect of the element of data. The element of data and the knowledge point may be linked with a traversable link. The knowledge point may further be linked to a second element of data. Natural language processing analysis, linguistic analysis, sentiment analysis, and metadata analysis, may be used to determine the aspect of the element of data. | 04-28-2016 |
20160117605 | Iterated Geometric Harmonics for Data Imputation and Reconstruction of Missing Data - Systems and methods for reconstruction of missing data using iterated geometric harmonics are described herein. A method includes receiving a dataset having missing entries, initializing missing values in the dataset with random data, and then performing the following actions for multiple iterations. The iterated actions include selecting a column to be updated, removing the selected column from the dataset, converting the dataset into a Gram matrix using a kernel function, extracting rows from the Gram matrix for which the selected column does not contain temporary values to form a reduced Gram matrix, diagonalizing the reduced Gram matrix to find eigenvalues and eigenvectors, constructing geometric harmonics using the eigenvectors to fill in missing values in the dataset, and filling in missing values to improve the dataset and create a reconstructed dataset. The result is a reconstructed dataset. The method is particularly useful in reconstructing image and video files. | 04-28-2016 |
20160117606 | Methods, systems, non-transitory computer readable medium, and machine for maintaining emotion data in a computing environment - This document discloses methods, systems, computer readable medium, and a machine for describing, mapping, modeling, generating, recreating, maintaining, archiving, and incorporating emotion or the immersive first-person experiences of self-awareness as data in a computing environment. In a preferable embodiment of the present invention, the method includes analyzing a body, obtaining information regarding at least some of the one or more relationships corresponding to location, topological, directional, distance, or temporal references, and generating a representation of the experience. In a preferable embodiment of the present invention, one or more methods also include the representation of immersive first-person experiences of emotion with other systems and data, the step of using comprising at least one of editing, generating, storing, converting, encoding, transmitting, displaying, editing, and incorporating data from input, output, outcome, result, or derivative values with applications, systems, or instance relevant computing environments. | 04-28-2016 |
20160117607 | Learning Device Interaction Rules - Devices and methods are disclosed for establishing interaction among electronic devices of an environment. The device has a transmitter, receiver, memory for storing interaction rules, and a processor for learning the interaction rules in association with the transmitter, receiver, and other devices of the environment. The device also includes components for performing the device specific functions and a state sensor for determining the logical or physical state of the device. Methods involve observing at one or more devices change of state activity among the plurality of devices through receiving a change of state message that is transmitted to the one or more devices. A set of rules are learned at the one or more devices based upon observing the change of state activity. The learned set of rules are then applied at the one or more devices to automatically control changes of state of devices within the plurality of devices. | 04-28-2016 |
20160117609 | ECONOMIC OPTIMIZATION FOR PRODUCT SEARCH RELEVANCY - In one embodiment, a method is illustrated as including defining a set of perspective objects capable of being placed onto a modified web page, monitoring parameters of a web page, the parameters including a number of times a current object is executed on the web page, using an Artificial Intelligence (AI) algorithm to determine a perspective object with a preferred Return On Investment (ROI), and selecting the perspective object to be placed onto the modified web page. | 04-28-2016 |
20160123617 | TEMPERATURE PREFERENCE LEARNING - A method for relative temperature preference learning is described. In one embodiment, the method includes identifying one or more current settings of a thermostat located at a premises, identifying one or more current indoor and outdoor conditions, calculating a current indoor differential between the current indoor temperature and the current target temperature, calculating a current outdoor differential between the current outdoor temperature and the current target temperature, and learning temperature preferences based on an analysis of the one or more current indoor conditions and the one or more current outdoor conditions. The one or more current settings of the thermostat include at least one of a current target temperature, current runtime settings, and current airflow settings. The one or more current indoor and outdoor conditions include at least one of a current temperature, current humidity, current indoor airflow, current atmospheric pressure, current level of precipitation, and current cloud cover. | 05-05-2016 |
20160124933 | GENERATION APPARATUS, GENERATION METHOD, AND PROGRAM - Aspects of the present invention disclose a method, computer program product, and system for generating target text based on target data. The method includes one or more processors decomposing one or more portions of text into at least one corresponding keyword and at least one corresponding template. The method further includes learning a classification model associated with selecting a template based on a category of a keyword. The method further includes identifying a target keyword that is represented by target data. The method further includes selecting a target template that is used to represent the target data based on a category associated with the identified target keyword utilizing the classification model. The method further includes generating target text that represents the target data based on the selected text template based on the selected target template and the identified target keyword. | 05-05-2016 |
20160124945 | METHOD AND SYSTEM FOR MACHINE COMPREHENSION - The AKOS (Artificial Knowledge Object System) of the invention is a software processing engine that relates incoming information to pre-existing stored knowledge in the form of a world model and, through a process analogous to human learning and comprehension, updates or extends the knowledge contained in the model, based on the content of the new information. Incoming information can come from sensors, computer to computer communication, or natural human language in the form of text messages. The software creates as an output. Intelligent action is defined as an output to the real-world accompanied by an alteration to the internal world model which accurately reflects an expected, specified outcome from the action. These actions may be control signals across any standard electronic computer interface or may be direct communications to a human in natural language. | 05-05-2016 |
20160124951 | ANSWER SEQUENCE DISCOVERY AND GENERATION - Aspects of the present disclosure are directed toward discovering and generating answer sequences. Aspects are directed toward parsing, by a natural language processing technique configured to analyze syntactic and semantic content, a corpus of data for a subject matter. Aspects are also directed toward detecting a first set of answers including a first answer corresponding to a first answer category and a second set of answers including a second answer corresponding to a second answer category. Both the first and the second answer categories may relate to the subject matter. Aspects are also directed toward identifying a first set of ordering data for the first set of answers and the second set of answers. Aspects are also directed toward determining a first answer sequence corresponding to an order of the first set of answers and the second set of answers. | 05-05-2016 |
20160124952 | Using Synthetic Events to Identify Complex Relation Lookups - An approach is provided in which a knowledge manager analyzes a corpus of documents based upon relations corresponding to entities in a structured resource and constructs a natural language context associated with a set of the relations. The knowledge manager maps the natural language context to a database query and, in turn, invokes the database query when the knowledge manager matches a question to the natural language context. | 05-05-2016 |
20160125039 | Data mining method and apparatus, and computer program product for carrying out the method - A distributed data mining method to be carried out in user equipments connected to a peer-to-peer communication network. The method includes: providing a data mining frame application in the equipments as code running on a device-specific platform and a trainable data mining algorithm produced on a programming language common to all equipments, running the data mining algorithm in a first equipment to process user data stored therein, modifying the data structures and/or the input parameter set of the data mining algorithm through training, forwarding at least a part of the modified input parameter set and/or the modified data structures of the data mining algorithm as training information from the first equipment to at least one second equipment, and modifying the input parameter set and/or the data structures of the data mining algorithm running on at least one second equipment using the training information received from the first equipment. | 05-05-2016 |
20160125292 | APPARATUS AND METHOD FOR GENERATING PREDICTION MODEL - Disclosed herein are an apparatus for generating a prediction model and a method thereof. The apparatus for generating a prediction model from data composed of a plurality of instances each including one or more predictor values and a target value includes a pre-processing module configured to generate pre-processed target values by calculating weighted averages of the target values for a predetermined prediction period and subtracting the weighted averages from the target values, a prediction model generation module configured to calculate prediction values of the target values of the respective instances from the plurality of instances including the pre-processed target values, and a post-processing module configured to add the weighted averages, which are subtracted in the pre-processing module, to the prediction values of the target values of the respective instances. | 05-05-2016 |
20160125299 | APPARATUS FOR DATA ANALYSIS AND PREDICTION AND METHOD THEREOF - An apparatus and a method for data analysis and prediction, the apparatus including: a data collection unit configured to collect a prediction value derived through a machine learning of input data; a candidate prediction value generation unit configured to generate a candidate prediction value; a rule verification value configured to store one or more rules and verify whether the candidate prediction value violates the one or more rules; and an evaluation unit configured to calculate a fitness of the candidate prediction value according to an error rate of the candidate prediction value with respect to the prediction value and a verification result of the rule verification unit. | 05-05-2016 |
20160125307 | AIR QUALITY INFERENCE USING MULTIPLE DATA SOURCES - The use of data from multiple data source provides inferred air quality indices with respect to a particular pollutant for multiple areas without the addition of air quality monitor stations to those areas. Labeled air quality index data for a pollutant in a region may be obtained from one or more air quality monitor stations. Spatial features for the region may be extracted from spatially-related data for the region. The spatially-related data may include information on fixed infrastructures in the region. Likewise, temporal features for the region may be extracted from temporally-related data for the region that changes over time. A co-training based learning framework may be further applied to co-train a spatial classifier and a temporal classifier based at least on the labeled air quality index data, the spatial features for the region, and the temporal features for the region. | 05-05-2016 |
20160125308 | DATA COMPARISON METHOD - The invention concerns a method to compare two data obtained from a sensor or interface, carried out by processing means of a processing unit, the method comprising the computing of a similarity function between two feature vectors of the data to be compared, | 05-05-2016 |
20160125312 | SYSTEM AND METHOD FOR A DEVICE TO WORK COLLABORATIVELY WITH AN EXPERT - A system and method in which a device will search for and work collaboratively with an expert to respond to a request that the device is unable to respond to on its own. The expert may be one or more of, or a combination of, a human, a virtual persona, a robot or another device. | 05-05-2016 |
20160125313 | System and Methodology to Handle Misdirected Input Data During Multi Partitioned Real Time Analytics - A mechanism is provided in a stream computing platform for data stream change detection and model swapping. The mechanism builds a model for each input data stream in a stream computing platform. Each tuple of each given input data stream is tagged with a key corresponding to the given input data stream. The mechanism performs an operation on each input data stream using its corresponding model. The mechanism detects a misdirected input data stream, which is tagged with a key that does not correspond to the misdirected input data stream. The mechanism pauses the misdirected input data. stream swaps a model corresponding to the misdirected input data stream with another model corresponding to another paused input data stream. | 05-05-2016 |
20160125314 | SYSTEMS AND METHODS FOR NATIVE ADVERTISEMENT SELECTION AND FORMATTING - Provided herein is a system or method for a native advertisement selection and formatting module operable to monitor displayable content and store characteristic-related information relating to the monitored content, including keyword-related information and format-related information, utilize one or more machine learning-based algorithms, analyze the characteristic-related information relating to the monitored content, including the keyword-related information and the format-related information, and based in part on the analysis, output detailed contextual settings, and select and format native advertisements to be displayed in visual association with the displayable content, based in part on the detailed contextual settings. | 05-05-2016 |
20160125315 | System and Methodology to Handle Misdirected Input Data During Multi Partitioned Real Time Analytics - A mechanism is provided in a stream computing platform for data stream change detection and model swapping. The mechanism builds a model for each input data stream in a stream computing platform. Each tuple of each given input data stream is tagged with a key corresponding to the given input data stream. The mechanism performs an operation on each input data stream using its corresponding model. The mechanism detects a misdirected input data stream, which is tagged with a key that does not correspond to the misdirected input data stream. The mechanism pauses the misdirected input data. stream swaps a model corresponding to the misdirected input data stream with another model corresponding to another paused input data stream. | 05-05-2016 |
20160125316 | MALT: Distributed Data-Parallelism for Existing ML Applications - Systems and methods are disclosed for parallel machine learning with a cluster of N parallel machine network nodes by determining k network nodes as a subset of the N network nodes to update learning parameters, wherein k is selected to disseminate the updates across all nodes directly or indirectly and to optimize predetermined goals including freshness, balanced communication and computation ratio in the cluster; sending learning unit updates to fewer nodes to reduce communication costs with learning convergence; and sending reduced learning updates and ensuring that the nodes send/receive learning updates in a uniform fashion. | 05-05-2016 |
20160125317 | SYSTEM AND METHOD FOR TRANSACTION LEARNING - A system and method a method for providing for providing personalized transaction learning and tagging. The method may include tagging transactions associated with one or more financial accounts belonging to an account holder, whether the account holder be the primary, secondary, or a related account holder, such as a spouse, parent, guardian, and the like. The method may include linking all accounts belong to and/or associated with an account holder and receiving transaction data from each linked account, including, for example, transaction date, transaction time, transaction amount, merchant name, merchant location, merchant identifier, account number used in transaction, SKU-level transaction information, and/or other purchase identifiers (e.g., merchant-provided product/service name, account holder-provided product/service name, and the like). Once the system receives the transaction data, the system may query the account holder for input regarding the transaction data. The input may include tagging the transaction as belonging to a particular spending category and/or affirming or denying that the transaction belongs to a particular category. The system may receive and create categories based on account holder data, demographic data, credit data, and account holder profile data. | 05-05-2016 |
20160132640 | SYSTEM, METHOD AND COMPUTER READABLE MEDIUM FOR RAPID DNA IDENTIFICATION - An extremely efficient method and system for identifying an unknown DNA sample based on probabilistic data structures and machine learning techniques. The method and system can quickly and accurately determine a sample's most likely species, sub-species, or strain. The method and system can identify unknown DNA samples with high accuracy and efficiency (reduced time and resources) without requiring alignment. As such, the method and system is suited to develop innovative applications for, but not limited thereto, many clinical, agricultural, environmental and military/forensic scenarios where the rapid classification of DNA may be of critical utility. | 05-12-2016 |
20160132774 | METHOD AND SYSTEM FOR PREDICTING A GEOGRAPHIC LOCATION OF A NETWORK ENTITY - A method and system for predicting the geographic location of a network entity are described. Examples include predicting the geographic location of a network entity by directing the network entity to transmit one or more data packets to a number of predetermined network identifiers, such as IP addresses, where data corresponding to each of the network identifiers is part of a geographic location prediction model. In examples, a dataset that represents transit times for the data packets transmitted from the network entity to the hosts identified by the IP addresses is determined, and a geographic location for the network entity is predicted by applying the geographic location prediction model to the dataset. | 05-12-2016 |
20160132775 | WEIGHT ADJUSTED COMPOSITE MODEL FOR FORECASTING IN ANOMALOUS ENVIRONMENTS - A method, system, and computer program product for weight adjusted composite model for forecasting in anomalous environments are provided in the illustrative embodiments. A base forecasting model and a second forecasting model are combined to form a composite model, the base forecasting model configured to forecast an event in a time series, the second forecasting model configured to represent an anomalous portion of data in the time series. A mixing algorithm is combined with the composite model to adjust a set of weights associated with the composite model. Upon identifying a future period in which the event is to be forecasted, using the mixing algorithm, a subset of the set of weights is adjusted to from a weight adjusted composite model. The weight adjusted composite model is executed to forecast the event in the future period. | 05-12-2016 |
20160132777 | BELIEF DATA MODEL MANAGEMENT SERVICE - In general, embodiments of the present invention provide systems, methods and computer readable media for providing a belief data modeling service for representing and operating belief models data. In embodiments, a belief data modeling service may be configured to perform operations comprising receiving a belief data modeling service request including an input data model representing a set of data, a set of input parameters including at least one of observational data and modeling data, and an operation to be applied to the input data model using the input parameters; and, in response to receiving the belief data modeling service request, generating an output belief data model of the set of entities by generating new states and a set of logical conditionals that constrain the states for at least a subset of the entities by applying the input operation to the input data model. | 05-12-2016 |
20160132782 | ESTIMATION OF A DELETED FLUID CONSUMPTION - A device for estimating a deleted fluid consumption during a deletion phase, where said device comprises: a collection module configured to collect: a) first consumption data comprising information about the consumption of fluid from n fluid meters coming from a first group, and b) second consumption data comprising information about the consumption of fluid from m fluid meters coming from a second group, a computer analysis module which is configured for calculating, as a function of the first and second consumption data weighting coefficients β | 05-12-2016 |
20160132786 | PARTITIONING DATA FOR TRAINING MACHINE-LEARNING CLASSIFIERS - Various embodiments relating to partitioning a data set for training machine-learning classifiers based on an output of a globally trained machine-learning classifier are disclosed. In one embodiment, a first machine-learning classifier may be trained on a set of training data to produce a corresponding set of output data. The set of training data may be partitioned into a plurality of subsets based on the set of output data. Each subset may correspond to a different class. A second machine-learning classifier may be trained on the set of training data using a plurality of classes corresponding to the plurality of subsets to produce, for each data object of the set of training data, a probability distribution having for each class a probability that the data object is a member of the class. | 05-12-2016 |
20160132787 | DISTRIBUTED, MULTI-MODEL, SELF-LEARNING PLATFORM FOR MACHINE LEARNING - A system is provided for multi-methodology, multi-user, self-optimizing Machine Learning as a Service for that automates and optimizes the model training process. The system uses a large-scale distributed architecture and is compatible with cloud services. The system uses a hybrid optimization technique to select between multiple machine learning approaches for a given dataset. The system can also use datasets to transferring knowledge of how one modeling methodology has previously worked over to a new problem. | 05-12-2016 |
20160132788 | METHODS AND SYSTEMS FOR CREATING A CLASSIFIER CAPABLE OF PREDICTING PERSONALITY TYPE OF USERS - The disclosed embodiments illustrate methods and systems for creating a classifier for predicting a personality type of users. The method includes receiving a first tag for messages, from a crowdsourcing platform. The first tag relates to personality type of users. Further, the messages, tagged with first tag are segregated into a training data and a testing data. Further, parameters associated with set of messages in the training data are determined based on type of messages. Further, classifiers are trained for a personality type. Further, a second tag for set of messages in testing data is predicted using trained classifiers for a combination of parameters. A performance of classifiers is determined by comparing the second tag and the first tag associated with set of messages in the testing data. A classifier is selected from classifiers, which is indicative of a best combination of parameters to predict personality type of users. | 05-12-2016 |
20160140217 | TEXT MATCHING DEVICE AND METHOD, AND TEXT CLASSIFICATION DEVICE AND METHOD - [Object] To provide a system for automatically and reliably collecting information belonging to a given category, and matching the information appropriately in a timely manner. | 05-19-2016 |
20160140438 | Hyper-class Augmented and Regularized Deep Learning for Fine-grained Image Classification - Systems and methods are disclosed for training a learning machine by augmenting data from fine-grained image recognition with labeled data annotated by one or more hyper-classes, performing multi-task deep learning; allowing fine-grained classification and hyper-class classification to share and learn the same feature layers; and applying regularization in the multi-task deep learning to exploit one or more relationships between the fine-grained classes and the hyper-classes. | 05-19-2016 |
20160140440 | REAL-TIME PROACTIVE MACHINE INTELLIGENCE SYSTEM BASED ON USER AUDIOVISUAL FEEDBACK - Disclosed herein are techniques for implementing a machine intelligence computer system that can proactively monitor user audiovisual feedbacks as ques for improving the machine learning and predictive data analytical processes. Based on the real-time feedbacks, the introduced proactive machine intelligence system (PMIS) can dynamically revise (e.g., by assigning different weights) and/or filter the gathered input data for machine learning purposes. The PMIS can also dynamically adjust the machine learning algorithms adapted in the predictive models based on user real-time feedbacks. | 05-19-2016 |
20160140451 | SYSTEM AND METHOD FOR LARGE-SCALE MULTI-LABEL LEARNING USING INCOMPLETE LABEL ASSIGNMENTS - At least one label prediction model is trained, or learned, using training data that may comprise training instances that may be missing one or more labels. The at least one label prediction model may be used in identifying a content item's ground-truth label set comprising an indicator for each label in the label set indicating whether or not the label is applicable to the content item. | 05-19-2016 |
20160140452 | METHODS AND SYSTEMS USING A COMPOSITION OF AUTONOMOUS SELF-LEARNING SOFTWARE COMPONENTS FOR PERFORMING COMPLEX REAL TIME DATA-PROCESSING TASKS - A composition of autonomous self-learning skill software components interact with one another to solve a real time complex task. Each software component includes a knowledge base and an inference algorithm that uses the knowledge base to solve a given data-processing task involving input data. Each software component may also include a machine learning algorithm for training the knowledge base with new data in real-time as the new data is received. | 05-19-2016 |
20160140454 | User Interest Learning through Hierarchical Interest Graphs - User interest learning through hierarchical interest graph techniques are described. In one or more implementations, each of a plurality of categories in a directed hierarchical interest graph are assigned a distance value which represents a shortest distance in the directed hierarchical interest graph from a root category to the category. A list of keywords is formed from user data that denotes a corresponding said category and frequency of the category. A maximum of the frequencies amongst the plurality of categories is determined and a score is calculated for each of the keywords based on the frequency of the category, the maximum of the frequencies, and the distance value for the keyword. Increments of scores may be propagated from child categories to parent categories in the hierarchical interest graph such that greater weighting is given to child categories that are less abstract than parent categories in the directed graph. Further, the scores of the plurality of categories may be adjusted based on subsequent scores calculated from subsequent user data. | 05-19-2016 |
20160142266 | EXTRACTING DEPENDENCIES BETWEEN NETWORK ASSETS USING DEEP LEARNING - A network analysis tool receives network flow information and uses deep learning—machine learning that models high-level abstractions in the network flow information—to identify dependencies between network assets. Based on the identified dependencies, the network analysis tool can discover functional relationships between network assets. For example, a network analysis tool receives network flow information, identifies dependencies between multiple network assets based on evaluation of the network flow information, and outputs results of the identification of the dependencies. When evaluating the network flow information, the network analysis tool can pre-process the network flow information to produce input vectors, use deep learning to extract patterns in the input vectors, and then determine dependencies based on the extracted patterns. The network analysis tool can repeat this process so as to update an assessment of the dependencies between network assets on a near real-time basis. | 05-19-2016 |
20160147799 | RESOLUTION OF DATA INCONSISTENCIES - Examples disclosed herein enable identifying a feature that is common to a first dataset and a second dataset, wherein a first value of the feature in the first dataset is different from a second value of the feature in the second dataset; determining a first predicted value of the feature in the first dataset based on a second dataset classifier trained on the second dataset; determining a second predicted value of the feature in the second dataset based on a first dataset classifier trained on the first dataset; determining a first similarity score between the first value and the first predicted value; determining a second similarity score between the second value and the second predicted value; and generating a bipartite graph that comprises a first node indicating the first value, a second node indicating the second value, and an edge indicating the first or second similarity score. | 05-26-2016 |
20160147847 | PRESENTING ANTICIPATED USER SEARCH QUERY RESULTS PROMPTED BY A TRIGGER - A method for presenting search query results is provided. The method may include detecting an occurrence of the trigger event. The method may include determining a category of information based on data associated with the trigger event. The method may include identifying at least one constraint based on the determined category of information. The method may include appending to the identified at least one constraint to the determined category of information. The method may include generating at least one search query. The method may include selecting at least one candidate website based on the category of information. The method may include performing the at least one search query on the at least one candidate website. The method may include filtering each search query result within the search query results. The method may include sending each filtered search query result within the search query results to a user. | 05-26-2016 |
20160147874 | Generating Derived Links - A system, method, computer program product and computer program for evaluating links between objects are provided. A receive ontology component receives an ontology and an identify component identifies, from the ontology, semantic feature types within the ontology that can be used to measure the links between the objects. A data receive component receives instance information and maps the instance information into an ontological form of the instance information. An analyze component analyzes the ontological form to generate an ontological mapping of the instance information. A match component analyzes the mapping to identify matches with semantic patterns. A strength component analyzes the associated semantic features associated with the objects of the matches to determine weightings for the links of the matches. An alert component provides the links and associated weightings. | 05-26-2016 |
20160148100 | APPARATUS AND METHOD FOR CHANGING ALARM INFORMATION IN ACCORDANCE WITH WEATHER - The present invention relates to an apparatus and method for changing alarm information based on weather, and more particularly to an apparatus and method for changing alarm information based on weather, which can collect locations, time and weather information for a path, routinely used by a user, and a destination and actively adjust alarm time based on a weather condition, which can measure the gap between a weather forecast and real-time observation data, determine whether a gap notification condition has been satisfied and provide notification to a user, and which can collect location information and weather information via a report message, determine the urgency level of a disaster situation and generate a disaster information list that enables the disaster situation to be effectively determined. | 05-26-2016 |
20160148101 | KNOWLEDGE EVALUATION APPARATUS, METHOD, AND SYSTEM - A knowledge evaluation apparatus, method, and system are provided. The knowledge evaluation apparatus includes an extraction module configured to extract at least one of triple information which is previously set with respect to a document for each field accumulated in an Internet community site; a classification module configured to classify and digitize the triple information extracted from the document for each field by a topic and an item; and a measurement module configured to measure knowledge maturity of the Internet community site based on the knowledge classified and digitized by the classification module. | 05-26-2016 |
20160148104 | SYSTEM AND METHOD FOR PLANT MONITORING - A system and method for automatic plant monitoring. The method comprises: identifying at least one test input respective of a test area, wherein the test area includes at least one part of a plant; and generating a plant condition prediction based on the at least one test input and on a prediction model, wherein the prediction model is based on a training set including at least one training input and at least one training output, wherein each training output corresponds to a training input. | 05-26-2016 |
20160148113 | SYSTEMS AND METHODS FOR DETERMINING DISAGGREGATED ENERGY CONSUMPTION BASED ON LIMITED ENERGY BILLING DATA - Various embodiments of the present disclosure can include systems, methods, and non-transitory computer readable media configured to train a Bayesian network model based on a given set of data. Information associated with a user can be received. The information can include aggregated energy consumption data at one or more low frequency time intervals. At least a portion of the information can be inputted into the Bayesian network model. A plurality of energy consumption values for a plurality of energy consumption sources associated with the user can be inferred based on inputting the at least the portion of the information into the Bayesian network model. | 05-26-2016 |
20160148116 | EXTRACTION OF SEMANTIC RELATIONS USING DISTRIBUTIONAL RELATION DETECTION - According to an aspect, a pair of related entities that includes a first entity and a second entity is received. Distributional relations are detected between the first entity and the second entity. The detecting includes identifying two sets of entities in a corpus, the first set including the first entity and at least one other entity that is semantically similar to the first entity, and the second set including the second entity and at least one other entity that is semantically similar to the second entity. Semantic relations are detected between entities in the first set and entities in the second set. A relation classifier is trained using the pair of related entities and detected semantic relations. The relation classifier model is applied to a new pair of entities to determine a likelihood of a semantic relation between the entities in the new pair of entities. | 05-26-2016 |
20160148117 | ATTRIBUTE ESTIMATION SYSTEM - The present invention provides an attribute estimation system capable of acquiring an image of a person whose attributes are to be estimated accurately and informing the person of an attribute estimation result. The attribute estimation system ( | 05-26-2016 |
20160148118 | Expert System And Data Analysis Tool Utilizing Data As A Concept - Methods and systems are provided for a tool that operates on a local user machine that is coupled to a remote storage. Data may be aggregated by the tool by combining raw data from multiple sources with different file types, where the data is stored in remote storage. Concepts and relationships existent within the inputted/loaded data may be learned by the tool. The data may be reconciled using the tool by a process of data scrubbing. The data may be analyzed using data manipulation techniques and statistical analysis. The work data flow from the data analysis may be captured by the tool and stored in the remote storage for later use. Visualizations (e.g., charts and graphs) may be generated by the tool for the analyzed data. | 05-26-2016 |
20160148119 | STATISTICAL PATTERN GENERATION FOR INFORMATION EXTRACTION - An apparatus for extracting selected information from a set of symbols includes said alignment module is configured to retrieve test patterns from a symbol input, and to attempt alignment of test patterns with a canonical pattern. Successful alignment between a particular test pattern and said canonical pattern indicates of existence of information of interest in a particular candidate pattern. Upon detection of a successful alignment, the alignment module passes information concerning the test pattern to a user. Additionally, in response to detecting an unsuccessful attempt to align the first test pattern and the canonical pattern, said alignment module passes, to said user, information concerning the first test pattern. | 05-26-2016 |
20160148120 | CALCULATION APPARATUS, CALCULATION METHOD, LEARNING APPARATUS, LEARNING METHOD, AND PROGRAM - A calculation apparatus includes a feature vector acquisition unit for acquiring a feature vector that corresponds to each alternative of a plurality of choice sets; an absolute evaluation calculation unit for calculating an absolute evaluation vector that represents an absolute evaluation of alternatives independent of a combination of the plurality of alternatives; a relativized-matrix calculation unit for calculating a relativized matrix that represents relative evaluations of the plurality of alternatives in a choice set; and a relative evaluation calculation unit for calculating a relative evaluation vector that represents a relative evaluation of each alternative of the plurality of alternatives from a product of multiplying the relativized matrix by the absolute evaluation vector. The calculation apparatus can predict intransitive choices of a person who exhibits intransitive preferences. | 05-26-2016 |
20160150041 | METHOD, SYSTEM, AND PROGRAM STORAGE DEVICE FOR UNIFIED CONTENT STORAGE IN SESSION-BASED SERVICES - At a session context proxy component, a first communication destined for a first session manager is intercepted. It may be identified that the first communication references a first piece of content. A request may be sent to a content access monitor to obtain metadata about the first piece of content. The metadata about the first piece of content is then sent to the first session manager for storage with session information for the first communication. A second communication destined for a second session manager is intercepted, the second communication in a different communications modality than the first communication. It may be identified that the second communication references a second piece of content. A request may be sent to the content access monitor to obtain metadata about the second piece of content. The metadata about the second piece of content is then sent to the second session manager for storage with session information for the second communication. | 05-26-2016 |
20160155045 | TRANSFORM FOR A NEUROSYNAPTIC CORE CIRCUIT | 06-02-2016 |
20160155046 | TRANSFORM ARCHITECTURE FOR MULTIPLE NEUROSYNAPTIC CORE CIRCUITS | 06-02-2016 |
20160155052 | INDENTIFYING LOCATIONS OF POTENTIAL USER ERRORS DURING MANIPULATION OF MULTIMEDIA CONTENT | 06-02-2016 |
20160155054 | Method of Operating a Solution Searching System and Solution Searching System | 06-02-2016 |
20160155055 | Method of Operating a Solution Searching System and Solution Searching System | 06-02-2016 |
20160155062 | CONTINUOUS-TIME BAUM-WELCH TRAINING | 06-02-2016 |
20160155063 | Iterative Classifier Training on Online Social Networks | 06-02-2016 |
20160155064 | BAYES NETWORK FOR TARGET IDENTIFICATION | 06-02-2016 |
20160155065 | GENERATING DYNAMICALLY CONTROLLABLE COMPOSITE DATA STRUCTURES FROM A PLURALITY OF DATA SEGMENTS | 06-02-2016 |
20160155066 | DYNAMIC DATA STRUCTURES FOR DATA-DRIVEN MODELING | 06-02-2016 |
20160155067 | Mapping Documents to Associated Outcome based on Sequential Evolution of Their Contents | 06-02-2016 |
20160155068 | INFORMATION PROCESSING APPARATUS, INFORMATION PROCESSING METHOD, AND RECORDING MEDIUM FOR CLASSIFYING INPUT DATA | 06-02-2016 |
20160155069 | MACHINE LEARNING CLASSIFIER | 06-02-2016 |
20160155070 | DECISION TREE MACHINE LEARNING | 06-02-2016 |
20160162478 | INFORMATION TECHNOLOGY PLATFORM FOR LANGUAGE TRANSLATION AND TASK MANAGEMENT - A platform and related components are provided for managing and executing various processes involving distributed, crowd and automated resources, including human and machine language-based translation, are described, including methods and systems for creating and intelligently distributing cognizable translation units among internal workers, outsourcing centers, and crowd workers and methods and systems for on-demand translation. | 06-09-2016 |
20160162514 | IMAGE ANNOTATION USING AGGREGATED PAGE INFORMATION FROM ACTIVE AND INACTIVE INDICES - Architecture that addresses page information lost as part of a selection process in a search engine framework. An aggregation process collects all page or document information from the same image cluster and uses the aggregated page information to annotate one or more selected image-page pairs within the same image cluster. Once the entire set of descriptive terms is received, the entire set of descriptive terms or only an optimum set of top N descriptive terms of the entire set is for annotation of one or more of the representative images in the cluster. | 06-09-2016 |
20160162779 | DEVICE, SYSTEM AND METHOD FOR GENERATING A PREDICTIVE MODEL BY MACHINE LEARNING - A method of machine learning for generating a predictive model of a response characteristic based on historical data elements using a processor may include receiving historical data elements and historical values for the response characteristic related to uses of the historical data elements in web pages. A plurality of key-value pairs may be generated defining values of a plurality of predefined features representing properties of the historical data elements. Each of a plurality of n features may be represented by an axis in an n-dimensional space are extracted from the historical data elements. The extracted plurality of key-value pairs for each historical data element may be projected onto the n-dimensional space. The plurality of vectors may be input into a model generator to generate a predictive model predicting a value of the response characteristic for a new data element. | 06-09-2016 |
20160162785 | METHODS AND SYSTEMS FOR USING A USER CUSTOMIZABLE ARTIFICIAL INTELLIGENCE ENGINE FOR SEARCHING AND CORRELATING MULTIPLE DATABASES - The present invention discloses a portal capable of gathering and correlating data spread from multiple databases in multiple relevant but disparate information data sets, and an associated method. In some embodiments, the portal can relatively easy generate a new data set including a body of relevant but disparate information to be viewed through the prism of an individual's life. For example, by intuitively generating text-based and photo/video-based information of interest to the user measured by, for example, (i) periods of time (e.g., between the user's birth date and a milestone birthday, a wedding date, or an anniversary); (ii) geographic places (e.g., the user's hometown, the place he or she went to college, favorite vacation spots); and/or (iii) interests (e.g., favorite sports teams, foods, hobbies, etc.), as well as other variables that can be built into the search engine and database and/or provided by at least one user. | 06-09-2016 |
20160162793 | METHOD AND APPARATUS FOR DECISION TREE BASED SEARCH RESULT RANKING - A method of decision tree based search result ranking includes obtaining a training data set for generating at least one decision tree, the training data set having N training features and N greater than or equal to 2. The method further includes dividing a computational system of decision trees into N feature work groups corresponding to the N training features respectively, and by use of the feature work groups, computing splitting nodes and splitting values corresponding to the splitting nodes for the decision trees. The method also includes generating the decision trees using the computed splitting nodes and the corresponding splitting values; and ranking search results using the decision trees. | 06-09-2016 |
20160162794 | DECISION TREE DATA STRUCTURES GENERATED TO DETERMINE METRICS FOR CHILD NODES - Disclosed are various embodiments for data processing using decision tree data structures to implement artificial intelligence in an ingestion process. At least one computing device may be employed to access reference data from a data store accessible to the at least one computing device and parse the reference data using a natural language processor to identify relevant data for storage in at least one decision tree data structure. An ingestion process is applied to receive input data from at least one client device remotely over a transmission network. The at least one decision tree data structure is queried to identify a node in the at least one decision tree data structure that corresponds to a state of the ingestion process. A first metric for a first child node and a second metric for a second child node are generated using the input data. | 06-09-2016 |
20160162795 | SYSTEM AND METHOD FOR PROVIDING INTELLIGENT LOCATION INFORMATION - A method and system that includes extracting event models from at least one personal planning source of a user, wherein a parameter of an event model includes event location; periodically receiving location information of at least one mobile device of the user; storing the location information in a location log; a pattern worker module maintaining user location patterns through the location log; generating a location prediction from the extracted event models and the user location patterns; a first content worker module checking if the location prediction meets a set of content requirements; if the set of content requirements is satisfied, initiating content retrieval from at least one service; and pushing the content to the mobile device. | 06-09-2016 |
20160162800 | Parallel Development and Deployment for Machine Learning Models - Example systems and methods of developing a learning model are presented. In one example, a sample data set to train a first learning algorithm is accessed. A number of states for each input of the sample data set is determined A subset of the inputs is selected, and the sample data set is partitioned into a number of partitions equal to a combined number of states of the selected inputs. A second learning algorithm is created for each of the partitions, wherein each second learning algorithm receives the unselected inputs. Each of the second learning algorithms is assigned to a processor and trained using the samples of the partition corresponding to that algorithm. Decision logic is generated to direct each of a plurality of operational data units as input to one of the second learning algorithms based on states of the selected inputs of the operational data unit. | 06-09-2016 |
20160162801 | Quick Path to Train, Score, and Operationalize a Machine Learning Project - Automatically detecting and anticipating that an additional machine learning experiment may be needed. A method includes after successfully running a first experiment workflow, automatically prompting a user that an additional experiment workflow may be needed based on specific criteria associated with the first experiment workflow. The method further includes receiving input from the user confirming the additional experiment workflow. As a result of receiving input from the user confirming the additional experiment workflow, the method further includes the system automatically reconfiguring the first experiment workflow, including automatically identifying all necessary modules for the additional experiment workflow and connecting them properly to perform the intended second experiment workflow. The method further includes displaying to the user the first experimental workflow transitioning from the first experiment workflow to the additional experiment workflow. | 06-09-2016 |
20160162802 | Active Machine Learning - Technologies are described herein for active machine learning. An active machine learning method can include initiating active machine learning through an active machine learning system configured to train an auxiliary machine learning model to produce at least one new labeled observation, refining a capacity of a target machine learning model based on the active machine learning, and retraining the auxiliary machine learning model with the at least one new labeled observation subsequent to refining the capacity of the target machine learning model. Additionally, the target machine learning model is a limited-capacity machine learning model according to the description provided herein. | 06-09-2016 |
20160162804 | MULTI-TASK CONDITIONAL RANDOM FIELD MODELS FOR SEQUENCE LABELING - Embodiments of a computer-implemented method for automatically analyzing a conversational sequence between multiple users are disclosed. The method includes receiving signals corresponding to a training dataset including multiple conversational sequences; extracting a feature from the training dataset based on predefined feature categories; formulating multiple tasks for being learned from the training dataset based on the extracted feature, each task related to a predefined label; and providing a model for each formulated task, the model including a set of parameters common to the tasks. The set includes an explicit parameter, which is explicitly shared with each of the formulated tasks. The method further includes optimizing a value of the explicit parameter to create an optimized model; creating a trained model for the formulated tasks using the optimized value of the explicit parameter; and assigning predefined labels for the formulated tasks to a live dataset based on the corresponding trained model. | 06-09-2016 |
20160162805 | METHOD AND APPARATUS FOR CLASSIFYING DATA, AND METHOD AND APPARATUS FOR SEGMENTING REGION OF INTEREST (ROI) - A method and an apparatus are described to classify data. The method and apparatus includes selecting a hypothesis class among entire classes. The method and corresponding apparatus generate output data with regard to the entire classes by applying a classification algorithm to input data, and modify the input data to increase a value of the hypothesis class among the output data in response to a re-classification condition being met. The modified input data is set to be new input data. | 06-09-2016 |
20160162806 | Computer-Implemented Systems and Methods for Generating a Supervised Model for Lexical Cohesion Detection - Systems and methods are provided for a computer-implemented method for identifying pairs of cohesive words within a text. A supervised model is trained to detect cohesive words within a text to be scored. Training the supervised model includes identifying a plurality of pairs of candidate cohesive words in a training essay and an order associated with the pairs of candidate cohesive words based on an order of words in the training essay. The pairs of candidate cohesive words are filtered to form a set of evaluation pairs. The evaluation pairs are provided via a graphical user interface based on the order associated with the pairs of candidate cohesive words. An indication of cohesion or no cohesion is received for the evaluation pairs via the graphical user interface. The supervised model is trained based on the evaluation pairs and the received indications. | 06-09-2016 |
20160162807 | Emotion Recognition System and Method for Modulating the Behavior of Intelligent Systems - The disclosure describes an audio-based emotion recognition system that is able to classify emotions in real-time. The emotion recognition system, according to some embodiments, adjusts the behavior of intelligent systems, such as a virtual coach, depending on the user's emotion, thereby providing an improved user experience. Embodiments of the emotion recognition system and method use short utterances as real-time speech from the user and use prosodic and phonetic features, such as fundamental frequency, amplitude, and Mel-Frequency Cepstral Coefficients, as the main set of features by which the human speech is characterized. In addition, certain embodiments of the present invention use One-Against-All or Two-Stage classification systems to determine different emotions. A minimum-error feature removal mechanism is further provided in alternate embodiments to reduce bandwidth and increase accuracy of the emotion recognition system. | 06-09-2016 |
20160162808 | METHOD AND APPARATUS FOR ASSOCIATING MICRO-BLOGS WITH MEDIA PROGRAMS - A system that incorporates teachings of the present disclosure may operate, for example, obtaining a number of blogs including an initial set of annotated blogs and unannotated blogs. The initial set of annotated blogs are annotated as being either relevant to a selected media program or not relevant to the selected media program. A set of features is determined associating the selected media program with the unannotated blogs and a trained classifier is generated based on the set of features. The trained classifier is applied to the blogs to identify a subset of blogs relevant to the selected media program. An analysis is performed on the selected blogs to determine a trend related to the selected media program and a graphical user interface is presented that concurrently presents the selected blogs, the trend, and the selected media program. Other embodiments are disclosed. | 06-09-2016 |
20160171155 | SCALABLE PIPELINE FOR LOCAL ANCESTRY INFERENCE | 06-16-2016 |
20160171378 | TIME OUT-OF-HOME MONITORING | 06-16-2016 |
20160171380 | COLLABORATIVE PROFILE-BASED DETECTION OF BEHAVIORAL ANOMALIES AND CHANGE-POINTS | 06-16-2016 |
20160171385 | ANALYZING QUALITY OF APPLICATIONS LINKED TO AN ONLINE SYSTEM | 06-16-2016 |
20160171386 | CATEGORY AND TERM POLARITY MUTUAL ANNOTATION FOR ASPECT-BASED SENTIMENT ANALYSIS | 06-16-2016 |
20160171387 | DIGITAL COMPANIONS FOR HUMAN USERS | 06-16-2016 |
20160171388 | System for Refining Cognitive Insights Using Cognitive Graph Vectors | 06-16-2016 |
20160171389 | Method for Refining Cognitive Insights Using Cognitive Graph Vectors | 06-16-2016 |
20160171390 | Application Characterization for Machine Learning on Heterogeneous Core Devices | 06-16-2016 |
20160171391 | KNOWLEDGE DISCOVERY FROM CITATION NETWORKS | 06-16-2016 |
20160179835 | GENERATING USER RECOMMENDATIONS | 06-23-2016 |
20160180038 | DIAGNOSING AUTISM SPECTRUM DISORDER USING NATURAL LANGUAGE PROCESSING | 06-23-2016 |
20160180236 | FLEXIBLE INPUT SYSTEMS AND METHODS | 06-23-2016 |
20160180239 | MOTION DETECTION AND RECOGNITION EMPLOYING CONTEXTUAL AWARENESS | 06-23-2016 |
20160180243 | UNSUPERVISED TRAINING SETS FOR CONTENT CLASSIFICATION | 06-23-2016 |
20160180244 | AVOIDING SUPPORTING EVIDENCE PROCESSING WHEN EVIDENCE SCORING DOES NOT AFFECT FINAL RANKING OF A CANDIDATE ANSWER | 06-23-2016 |
20160180245 | METHOD AND SYSTEM FOR LINKING HETEROGENEOUS DATA SOURCES | 06-23-2016 |
20160180246 | Filtering Automated Selection of Keywords for Computer Modeling | 06-23-2016 |
20160180247 | Latency-Efficient Multi-Stage Tagging Mechanism | 06-23-2016 |
20160180248 | CONTEXT BASED LEARNING | 06-23-2016 |
20160180249 | AVOIDING SUPPORTING EVIDENCE PROCESSING WHEN EVIDENCE SCORING DOES NOT AFFECT FINAL RANKING OF A CANDIDATE ANSWER | 06-23-2016 |
20160180250 | SYSTEM AND A METHOD FOR LEARNING OF A RESOURCE MANAGEMENT SYSTEM USING QUANTIFIED GROUPS OF PROPERTIES | 06-23-2016 |
20160180251 | PROCESSING APPARATUS, PROCESSING METHOD, ESTIMATING APPARATUS, ESTIMATING METHOD, AND PROGRAM | 06-23-2016 |
20160180252 | EVALUATION SOLUTIONS OF OPTIMIZATION PROBLEMS | 06-23-2016 |
20160180253 | ITERATIVE REFINEMENT OF PATHWAYS CORRELATED WITH OUTCOMES | 06-23-2016 |
20160180254 | INFORMATION MATCHING APPARATUS, METHOD OF MATCHING INFORMATION, AND COMPUTER READABLE STORAGE MEDIUM HAVING STORED INFORMATION MATCHING PROGRAM | 06-23-2016 |
20160189037 | HYBRID TECHNIQUE FOR SENTIMENT ANALYSIS - One embodiment provides an apparatus. The apparatus includes a processor; at least one peripheral device coupled to the processor; a memory coupled to the processor; a generic sentiment model and a first domain training corpus stored in memory; and a hybrid sentiment analyzer logic stored in memory and to execute on the processor. The hybrid sentiment analyzer logic includes a sentiment lexicon generator logic to generate a domain sentiment lexicon based, at least in part, on the first domain training corpus and to store the domain sentiment lexicon in memory, a lexicon-based sentiment classifier logic to generate an annotated training corpus unsupervisedly, based, at least in part, on the domain sentiment lexicon and to store the annotated training corpus in memory, and a model-based sentiment adaptor logic to adapt the generic sentiment model based, at least in part, on the annotated training corpus to generate an adapted sentiment model and to store the adapted sentiment model in memory. | 06-30-2016 |
20160189040 | FILTERING AUTOMATED SELECTION OF HASHTAGS FOR COMPUTER MODELING - A social networking system receives messages from users that include hashtags. The social networking system may use a natural language model to identify terms in the hashtag corresponding to words or phrases of the hashtag. The words or phrases may be used to modify a string of the hashtag. The social networking system may also generate computer models to determine likely membership of a message with various hashtags. Prior to generating the computer models, the social networking system may filter certain hashtags from eligibility for computer modeling, particularly hashtags that are not frequently used or that more typically appear as normal text in a message instead of as a hashtag. The social networking system may also calibrate the computer model outputs by comparing a test message output with outputs of a calibration group that includes positive and negative examples with respect to the computer model output. | 06-30-2016 |
20160189041 | ANOMALY DETECTION FOR NON-STATIONARY DATA - A method of detecting anomalies in a time series is disclosed. A training time series corresponding to a process is extracted from an initial time series corresponding to the process, the training time series including a subset of the initial time series. Outlier data points in the training time series are modified based on predetermined acceptability criteria. A plurality of prediction methods are trained using the training time series. An actual data point corresponding to the initial time series is received. The plurality of prediction methods are used to determine a set of predicted data points corresponding to the actual data point. It is determined whether the actual data point is anomalous based on a calculation of whether each of the set of predicted data points is statistically different from the actual data point. | 06-30-2016 |
20160189045 | PREDICTING COMPUTER MODEL ACCURACY - A social networking system receives messages from users that include hashtags. The social networking system may use a natural language model to identify terms in the hashtag corresponding to words or phrases of the hashtag. The words or phrases may be used to modify a string of the hashtag. The social networking system may also generate computer models to determine likely membership of a message with various hashtags. Prior to generating the computer models, the social networking system may filter certain hashtags from eligibility for computer modeling, particularly hashtags that are not frequently used or that more typically appear as normal text in a message instead of as a hashtag. The social networking system may also calibrate the computer model outputs by comparing a test message output with outputs of a calibration group that includes positive and negative examples with respect to the computer model output. | 06-30-2016 |
20160189048 | DATA ANALYSIS SYSTEM AND METHOD - A data analysis system includes a modeling unit, a feature-extraction unit, a processing unit and an output unit. The modeling unit creates a prediction model by a machine learning algorithm according to training data. The feature-extraction unit extracts a plurality of fragment of feature data of input data, and classifies the feature data into a plurality of groups. The processing unit obtains a probability of the input data corresponding to the prediction model by the machine learning algorithm according to the feature of one of the groups, and determines the probability. When the probability is less than a predetermined value, the processing unit uses another feature data corresponding to another group which is not used to renew the probability of the input data corresponding to the prediction model through the machine learning algorithm. When the probability is greater than or equal to the predetermined value, the processing unit classifies the input data. The output unit outputs a classification result. | 06-30-2016 |
20160189056 | FAST EFFICIENT EVALUATION OF MESSAGES ON AUTOMOTIVE NETWORKS USING LOOK-UP TABLES - A Support Vector Machine (SVM) classifier employs a kernel ƒ(x | 06-30-2016 |
20160189057 | COMPUTER IMPLEMENTED SYSTEM AND METHOD FOR CATEGORIZING DATA - A self learning system and a method for categorizing input data have been disclosed. The system includes a generator that generates an initial training set comprising a plurality of words linked to scores/ratings which are based on the sentiments conveyed by the words. The words and corresponding ratings and sentiments are inter-linked and stored in a repository. A rule based classifier segregates the input data into individual words, and compares the words with the entries in the repository, and subsequently determines a first score corresponding to the input data. The input data is also provided to a machine-learning based classifier that generates a plurality of features corresponding to the input data and subsequently generates a second score corresponding to the input data. The first score and the second score are further aggregated by an ensemble classifier which further generates a classification score which enables the data to be classified into a plurality of predetermined categories. | 06-30-2016 |
20160189058 | INCREMENTAL LEARNER VIA AN ADAPTIVE MIXTURE OF WEAK LEARNERS DISTRIBUTED ON A NON-RIGID BINARY TREE - The present invention relates to a method for incremental learning of a classification model, where pre-defined weak incremental learners are distributed over the distinct regions in a set of partitionings of the input domain. The partitionings and regions are organized via a binary tree and they are allowed to vary in a data-driven way, i.e., in a way to minimize the classification error rate. Moreover, to test a given data point, a mixture of decisions is obtained through the models learned in the regions that this point falls in. Hence, naturally, in the cold start phase of the data stream, the simpler models belonging to the larger regions are favored and as more data get available, the invention automatically puts more weights on the more complex models. | 06-30-2016 |
20160189059 | FEATURE TRANSFORMATION LEARNING DEVICE, FEATURE TRANSFORMATION LEARNING METHOD, AND PROGRAM STORAGE MEDIUM - A feature transformation learning device includes an approximation unit, a loss calculation unit, an approximation control unit, and a loss control unit. The approximation unit takes a feature value that is extracted from a sample pattern and then weighted by a training parameter, assigns that weighted feature value to a variable of a continuous approximation function approximating a step function, and, by doing so, computes an approximated feature value. The loss calculation unit calculates a loss with respect to the task on the basis of the approximated feature value. The approximation control unit controls an approximation precision of the approximation function with respect to the step function such that the approximation function used with the approximation unit approaches the step function according to a decrease in the loss. The loss control unit updates the training parameter such that the loss decreases. | 06-30-2016 |
20160196492 | System and Method for Defining and Calibrating a Sequential Decision Problem using Historical Data | 07-07-2016 |
20160196495 | Mental Modeling Method And System | 07-07-2016 |
20160196498 | SYSTEM FOR GENERATING FABRICATED PATTERN DATA RECORDS | 07-07-2016 |
20160196503 | METHOD AND APPARATUS FOR RECOGNITION OF PATIENT ACTIVITY | 07-07-2016 |
20160196505 | INFORMATION PROCESSING APPARATUS, PROGRAM, AND INFORMATION PROCESSING METHOD | 07-07-2016 |
20160196506 | INCREMENTAL LEARNING MANAGEMENT DEVICE, INCREMENTAL LEARNING MANAGEMENT METHOD AND COMPUTER READABLE RECORDING MEDIUM STORING INCREMENTAL LEARNING MANAGEMENT PROGRAM | 07-07-2016 |
20160203404 | PREDICTING EXECUTION TIMES OF CONCURRENT QUERIES | 07-14-2016 |
20160203405 | TRANSFORMING PREDICTIVE MODELS | 07-14-2016 |
20160203414 | SELF-ADAPTIVE CLASSIFIERS | 07-14-2016 |
20160203416 | A METHOD AND SYSTEM FOR ANALYZING ACCESSES TO A DATA STORAGE TYPE AND RECOMMENDING A CHANGE OF STORAGE TYPE | 07-14-2016 |
20160203417 | SYSTEM AND METHOD FOR USING GRAPH TRANSDUCTION TECHNIQUES TO MAKE RELATIONAL CLASSIFICATIONS ON A SINGLE CONNECTED NETWORK | 07-14-2016 |
20160203418 | USER-GUIDED TEACHING AN OBJECT OF A DEICTIC REFERENCE TO A MACHINE | 07-14-2016 |
20160253595 | PRECISION AGRICULTURE SYSTEM | 09-01-2016 |
20160253596 | Geometry-directed active question selection for question answering systems | 09-01-2016 |
20160253597 | CONTENT-AWARE DOMAIN ADAPTATION FOR CROSS-DOMAIN CLASSIFICATION | 09-01-2016 |
20160253598 | LARGE-SCALE ANOMALY DETECTION WITH RELATIVE DENSITY-RATIO ESTIMATION | 09-01-2016 |
20160255037 | SYSTEM AND METHOD FOR PERSONALIZATION OF MESSAGE STREAMS VIA MACHINE LEARNING | 09-01-2016 |
20160379125 | PROVISIONING SERVICE REQUESTS IN A COMPUTER SYSTEM - Disclosed is a system, computer program product, and method for provisioning a new service request. The computer-implemented method begins with receiving a new service request for computational resources in a computing system. The required computational resources are memory usage, storage usage, processor usage, or a combination thereof to fulfill the new service request. Next a sandbox computing environment is used to operate the new service request. The sandbox computing environment is used to isolate the computing system. The sandbox computing environment produces a current computational resources usage data to fulfill the new service request in the sandbox computing environment. The current sandbox computational resources usage data and historical computational resources usage data are both used by a machine learning module to create a prediction of the computational resources that will be required in the computing system to fulfill the new service request. | 12-29-2016 |
20160379126 | RAPID TRAFFIC PARAMETER ESTIMATION - Data about vehicle movement at a stoplight are collected. A stoplight cycle time is predicted with a probability model. The data are compared to the predicted stoplight cycle time. A noise function is applied to the data to generate noise-applied data. The probability model for the predicted stoplight cycle time is updated by scaling the probability model with the noise-applied data to generate a new probability model. A recommended vehicle operation is provided via a network to at least one vehicle computer based on the predicted stoplight cycle time determined by the new probability model. | 12-29-2016 |
20160379128 | DISTRIBUTED AND PRIVACY-PRESERVING PREDICTION METHOD - Each computer of a peer-to-peer (P2P) network performs an iterative computer-based modeling task defined by a set of training data including at least some training data that are not accessible to the other computers of the P2P network, and by a set of parameters including a shared parameter. The modeling task optimizes an objective function comparing a model parameterized by the set of parameters with the training data. Each iteration includes: performing an iterative gradient step update of parameter values stored at the computer based on the objective function; receiving parameter values of the shared parameter from other computers of the P2P network; adjusting the parameter value of the shared parameter stored at the computer by averaging the received parameter values; and sending the parameter value of the shared parameter stored at the computer to other computers of the P2P network. | 12-29-2016 |
20160379132 | COLLABORATIVE FEATURE LEARNING FROM SOCIAL MEDIA - The present disclosure is directed to collaborative feature learning using social media data. For example, a machine learning system may identify social media data that includes user behavioral data, which indicates user interactions with content item. Using the identified social user behavioral data, the machine learning system may determine latent representations from the content items. In some embodiments, the machine learning system may train a machine-learning model based on the latent representations. Further, the machine learning system may extract features of the content item from the trained machine-learning model. | 12-29-2016 |
20160379133 | REASONING CLASSIFICATION BASED ON FEATURE PERTUBATION - Disclosed herein is a system and method that can be used with any underlying classification technique. The method takes into account both the value of the current feature vector. It is based on evaluating the effect of perturbing each feature by bootstrapping it with the negative samples and measuring the change in the classifier output. To assess the importance of a given feature value in the classified feature vector, a random negatively labeled instance is taken out of the training set and replaces the feature at question with a corresponding feature from this set. Then, by classifying the modified feature vector and comparing its predicted label and classifier output a user is able measure and observe the effect of changing each feature. | 12-29-2016 |
20160379134 | CLUSTER BASED DESKTOP MANAGEMENT SERVICES - Historical data and real-time data are collected for a plurality of computing resources. Based on the collected historical data, typical behavior of the plurality of computing resources is modeled and resulting models are stored in a model repository. With an inference engine, the real-time data is compared to the models. The plurality of computing resources are managed based on the comparing step. | 12-29-2016 |
20160379135 | JUST IN TIME CLASSIFIER TRAINING - Disclosed herein is a system and method that can be used with any underlying classification technique. The method receives a test dataset and determines the features in that test dataset that are present. From these features the training dataset is modified to only have those features that are present in the test dataset. This modified test dataset is then used to calibrate the classifier for the particular incoming data set. The process repeats itself for each different incoming dataset providing a just in time calibration of the classifier. | 12-29-2016 |
20160379136 | Methods and Systems for Automatic Extraction of Behavioral Features from Mobile Applications - An aspect computing device may be configured to perform program analysis operation in response to classifying a behavior as non-benign. The program analysis operation may identify new sequences of API calls or activity patterns that are associated with the identified non-benign behaviors. The computing device may learn new behavior features based on the program analysis operation or update existing behavior features based on the program analysis operation. For example, API sequences observed to occur when a non-benign behavior is recognized may be added to behavior features observed during program analysis operation. | 12-29-2016 |
20160379137 | MACHINE LEARNING CLASSIFICATION ON HARDWARE ACCELERATORS WITH STACKED MEMORY - A method is provided for processing on an acceleration component a machine learning classification model. The machine learning classification model includes a plurality of decision trees, the decision trees including a first amount of decision tree data. The acceleration component includes an acceleration component die and a memory stack disposed in an integrated circuit package. The memory die includes an acceleration component memory having a second amount of memory less than the first amount of decision tree data. The memory stack includes a memory bandwidth greater than about 50 GB/sec and a power efficiency of greater than about 20 MB/sec/mW. The method includes slicing the model into a plurality of model slices, each of the model slices having a third amount of decision tree data less than or equal to the second amount of memory, storing the plurality of model slices on the memory stack, and for each of the model slices, copying the model slice to the acceleration component memory, and processing the model slice using a set of input data on the acceleration component to produce a slice result. | 12-29-2016 |
20160379138 | CLASSIFYING TEST DATA BASED ON A MAXIMUM MARGIN CLASSIFIER - Systems and methods for classifying test data based on maximum margin classifier are described. In one implementation, the method includes obtaining training data having a predefined sample size, wherein the training data is composed of separable data-sets. For the training data, a Vapnik-Chervonenkis (VC) dimension for the training data is determined. For the VC dimension, an exact bound is subsequently determined. The exact bound may be minimized for obtaining the minimum VC classifier for predicting at least one class to which samples of the training data belong. | 12-29-2016 |
20160379139 | ADAPTIVE CLASSIFICATION OF DATA ITEMS - Described are embodiments for adaptive classification of data items which may include receiving a classification training set, the classification training set comprising a set of items associated with classification events made by a group of selected users, each item in the set of items having been designated as belonging to a particular classification by a selected user while manipulating the each item; determining from the classification training set a set of rules which can be used to classify unknown data items such that the classification of the unknown data items is consistent with the manual or automatic classification of the classification training set; adaptively updating the set of rules, according to classifications made to additional data items by additional users; and automatically classifying, based on the set of rules, one or more data items that are manipulated by a second set of one or more users. | 12-29-2016 |
20170235735 | SYSTEM AND METHODS OF GENERATING STRUCTURED DATA FROM UNSTRUCTURED DATA | 08-17-2017 |
20170235778 | INSTRUCTION ELEMENT VARIABILITY | 08-17-2017 |
20170236069 | SCALABLE SUPERVISED HIGH-ORDER PARAMETRIC EMBEDDING FOR BIG DATA VISUALIZATION | 08-17-2017 |
20170236070 | METHOD AND SYSTEM FOR CLASSIFYING INPUT DATA ARRIVED ONE BY ONE IN TIME | 08-17-2017 |
20170236072 | Robust Large-Scale Machine Learning in the Cloud | 08-17-2017 |
20170236073 | MACHINE LEARNED CANDIDATE SELECTION ON INVERTED INDICES | 08-17-2017 |
20170236074 | KERNEL PARAMETER SELECTION IN SUPPORT VECTOR DATA DESCRIPTION FOR OUTLIER IDENTIFICATION | 08-17-2017 |
20180023236 | SENSOR DATA LEARNING METHOD AND SENSOR DATA LEARNING DEVICE | 01-25-2018 |
20180024509 | SYSTEM MODELING, CONTROL AND OPTIMIZATION | 01-25-2018 |
20180024968 | SYSTEM AND METHOD FOR DOMAIN ADAPTATION USING MARGINALIZED STACKED DENOISING AUTOENCODERS WITH DOMAIN PREDICTION REGULARIZATION | 01-25-2018 |
20180025108 | COMPUTATIONAL METHOD FOR PREDICTING FUNCTIONAL SITES OF BIOLOGICAL MOLECULES | 01-25-2018 |
20180025279 | COGNITIVE COMPUTING FOR SERVERS AND MOBILE DEVICES | 01-25-2018 |
20180025281 | COGNITIVE COMPUTING FOR SERVERS AND MOBILE DEVICES | 01-25-2018 |
20180025282 | SYSTEMS AND METHODS OF DETERMINING SUFFICENT CAUSES FROM MULTIPLE OUTCOMES | 01-25-2018 |
20180025286 | DETECTING TRENDS IN EVOLVING ANALYTICS MODELS | 01-25-2018 |
20180025287 | USING PROXIES TO ENABLE ON-DEVICE MACHINE LEARNING | 01-25-2018 |
20180025289 | Performance Provisioning Using Machine Learning Based Automated Workload Classification | 01-25-2018 |
20180025290 | PREDICTIVE RISK MODEL OPTIMIZATION | 01-25-2018 |
20180027092 | SELECTING ASSETS | 01-25-2018 |
20190142291 | System and Method for Automatic Interpretation of EEG Signals Using a Deep Learning Statistical Model | 05-16-2019 |
20190146469 | SYSTEM AND METHOD FOR FACILITATING COMPREHENSIVE CONTROL DATA FOR A DEVICE | 05-16-2019 |
20190147112 | SYSTEMS AND METHODS FOR RANKING EPHEMERAL CONTENT ITEM COLLECTIONS ASSOCIATED WITH A SOCIAL NETWORKING SYSTEM | 05-16-2019 |
20190147297 | SYSTEM FOR TIME-EFFICIENT ASSIGNMENT OF DATA TO ONTOLOGICAL CLASSES | 05-16-2019 |
20190147300 | ANOMALY DETECTION IN MULTIDIMENSIONAL TIME SERIES DATA | 05-16-2019 |
20190147349 | MULTI-DIMENSIONAL COGNITION FOR UNIFIED COGNITION IN COGNITIVE ASSISTANCE | 05-16-2019 |
20190147353 | WATCHED HYPOTHESIS FOR DEEP QUESTION ANSWERING | 05-16-2019 |
20190147357 | AUTOMATIC DETECTION OF LEARNING MODEL DRIFT | 05-16-2019 |
20190147361 | LEARNED MODEL PROVISION METHOD AND LEARNED MODEL PROVISION DEVICE | 05-16-2019 |
20190147362 | SYSTEMS AND METHODS IMPLEMENTING AN INTELLIGENT OPTIMIZATION PLATFORM | 05-16-2019 |
20190147363 | ADAPTIVE KEY PERFORMANCE INDICATOR THRESHOLDS UPDATED USING TRAINING DATA | 05-16-2019 |
20190147364 | TECHNOLOGIES FOR PREDICTING COMPUTER HARDWARE PERFORMANCE WITH MACHINE LEARNING | 05-16-2019 |
20190147365 | DEEP VECTOR TABLE MACHINE SYSTEMS | 05-16-2019 |
20190147366 | Intelligent Recommendations Implemented by Modelling User Profile Through Deep Learning of Multimodal User Data | 05-16-2019 |
20190147367 | DETECTING INTERACTION DURING MEETINGS | 05-16-2019 |
20190147368 | PARKING AVAILABILITY PREDICTOR | 05-16-2019 |
20190147369 | Rule Determination for Black-Box Machine-Learning Models | 05-16-2019 |
20190147371 | TRAINING, VALIDATING, AND MONITORING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING MODELS | 05-16-2019 |
20190149626 | SIMILARITY LEARNING-BASED DEVICE ATTRIBUTION | 05-16-2019 |
20190149863 | USING MACHINE LEARNING AND OTHER MODELS TO DETERMINE A USER PREFERENCE TO CANCEL A STREAM OR DOWNLOAD | 05-16-2019 |
20220137611 | ABNORMALITY DETERMINATION APPARATUS, LEARNING APPARATUS AND ABNORMALITY DETERMINATION METHOD - According to one embodiment, a processing circuit classifies a time-series data corresponding to process amounts generated in a target facility into groups. For each of groups, the processing circuit applies time-series data included in the group to a first auto-encoder, which differs depending upon each group, and outputs time-series data. The processing circuit applies input difference data, which are based on output time-series data on the process amounts and the input time-series data, to a single second auto-encoder, and outputs difference data. The processing circuit determines an abnormality of the target facility, based on the comparison between addition data which are based on the output difference data and the output time-series data, and the input time-series data. | 05-05-2022 |
20220138492 | DATA PREPROCESSING AND REFINEMENT TOOL - A method of labeling building data comprises receiving, by a labeling system, a plurality of strings relating to data associated with a building, wherein the plurality of strings are received from one or more building devices, segregating the plurality of strings based on at least one semantic element included in the plurality of strings, clustering the plurality of strings based on at least one related element shared by at least a portion of the plurality of strings, labeling at least some of the plurality of strings based on the segregated and clustered plurality of strings, and generating a digital representation of the building using the labeled strings. | 05-05-2022 |
20220138503 | SYSTEM AND METHOD FOR LABEL GENERATION FOR TIMESERIES CLASSIFICATION - This disclosure relates generally to method and system for time series classification. Conventional methods for time-series classification requires substantial amount of annotated data for classification and label generation. The disclosed method and system are capable of generating accurate labels for time-series data by utilizing a small amount of representative data for each class. In an embodiment, the disclosed method generates a time-series data synthetically and associated labels by using a portion of the representative time-series data in each iteration, and self-correcting the generated labels based on a determination of quality of the generated labels using label quality checker models. | 05-05-2022 |
20220138509 | AUTONOMOUS AND CONTINUOUSLY SELF-IMPROVING LEARNING SYSTEM - A system and methods are provided in which an artificial intelligence inference module identifies targeted information in large-scale unlabeled data, wherein the artificial intelligence inference module autonomously learns hierarchical representations from large-scale unlabeled data and continually self-improves from self-labeled data points using a teacher model trained to detect known targets from combined inputs of a small hand labeled curated dataset prepared by a domain expert together with self-generated intermediate and global context features derived from the unlabeled dataset by unsupervised and self-supervised processes. The trained teacher model processes further unlabeled data to self-generate new weakly-supervised training samples that are self-refined and self-corrected, without human supervision, and then used as inputs to a noisy student model trained in a semi-supervised learning process on a combination of the teacher model training set and new weakly-supervised training samples. With each iteration, the noisy student model continually self-optimizes its learned parameters against a set of configurable validation criteria such that the learned parameters of the noisy student surpass and replace the learned parameter of the prior iteration teacher model, with these optimized learned parameters periodically used to update the artificial intelligence inference module. | 05-05-2022 |
20220138595 | COMPUTER-BASED IDENTIFICATION OF TYPES IN THE EMPERICAL SCIENCES BY TANGLE THEORY - A method of mechanically identifying and distinguishing clusters of qualities amongst potential qualities of given objects. Also a computer-readable storage device having stored thereon instructions for carrying out such method. Moreover, a system for mechanical exploitation of a data set containing relations of objects each with at least one respective quality, each object being an element of a given set of objects, the qualities each being included in a given list of potential qualities. | 05-05-2022 |
20220138597 | PROBLEM SOLVING USING SELECTED DATASETS OF INTERNET-OF-THINGS SYSTEM - A method, computer system, and a computer program product for problem solving using selected datasets from an internet-of-things system are provided. A trigger device in an internet-of-things system may be designated. The internet-of-things system may include the trigger device and internet-of-things devices. A trigger message may be received from the trigger device. A problem may be determined based on the trigger message. Data transmissions may be requested from the internet-of-things devices. Data may be received via the data transmission. The data transmission may be ended. A problem contributor may be identified by inputting the received data and the determined problem into a machine learning model. A problem response for responding to the problem may be generated. The generating may include inputting the problem contributor to the machine learning model. The problem response may be performed. | 05-05-2022 |
20220138614 | EXPLAINING MACHINE LEARNING BASED TIME SERIES MODELS - A method, computer system, and computer program product for explaining time series machine learning model are provided. The embodiment may include determining a first order difference in time series input data and historical training data. The embodiment may also include performing perturbation of time series input data based on the determined first order difference and the determined historical training data. The embodiment may further include computing closeness of the determined first order difference in the historical training data to the determined first order difference in the time series input data. The embodiment may also include generating a uniform random sample of first value input to a time series machine learning model. The embodiment may further include determining values of other inputs to the time series machine learning model based on the generated random sample and a random sample from the historical training data first order differences. | 05-05-2022 |
20220138615 | MACHINE LEARNING-BASED PRIVILEGE MODE - Embodiments of the present disclosure may relate to apparatus, process, or techniques to develop and to implement a machine learning-based privilege model to identify, for a given document production request, those documents that are privileged and do not need to be provided as part of the production request. In embodiments, during the training of the machine learning-based privilege model, each training document may be broken down into a pure text sub-document and a header only sub-document that includes, for example, email headers and their contents. The privilege model includes (1) a text model that is trained using pure text sub-documents, and (2) a header model that is trained using header only sub-documents, typically extracted from emails. Other embodiments may be described and/or claimed. | 05-05-2022 |
20220138617 | ARTIFICIAL INTELLIGENCE BASED APPLICATION MODERNIZATION ADVISORY - Technology for applying artificial intelligence to decide when to, and/or when not to, send a consumer of a computer system a communication recommending that the computer system be revised to include a more recent version of at least one of the following: a hardware component (for example, microprocessor(s)) and/or a software component (for example, an updated version of an app). The computer system, that is subject to modernization, may be owned outright by the consumer, or it may be purchased as a service (for example, infrastructure as a service, software as a service, package of cloud services). Some embodiments focus on modernization recommendations specifically tailored to cloud orchestration software that deploys containers. | 05-05-2022 |
20220138623 | METHOD FOR AND SYSTEM FOR PREDICTING ALIMENTARY ELEMENT ORDERING BASED ON BIOLOGICAL EXTRACTION - A system for predicting alimentary element ordering based on biological extraction, the system comprising a computing device configured to receive a biological extraction and alimentary element order chronicle of a user, retrieve an alimentary profile, identify, using the alimentary profile and a predictive machine-learning process, a predicted alimentary element and an alternative alimentary element, determine, using the predictive machine-learning process and the alimentary profile, the predicted alimentary element, select, using the predicted alimentary element, the alternative alimentary element, create a classifier, using a classification machine-learning process as a function of a plurality of alimentary element metrics, generate a plurality of related alimentary elements as a function of the classifier, rank the related alimentary elements as a function of the biological extraction, select the alternative alimentary element as a function of the ranking, and present the predicted alimentary element and the alternative alimentary element via a graphical user interface. | 05-05-2022 |
20220138629 | METHOD FOR AND SYSTEM FOR ARRANGING CONSUMABLE ELEMENTS WITHIN A DISPLAY INTERFACE - A system and method for arranging consumable elements within a display interface, the system including a computing device configured to receive a first consumable element; calculate a first consumable score as a function of the first consumable element; receive a second consumable element; calculate a second consumable score as a function of the second consumable element; and arrange the first consumable element and the second consumable element within a display interface as a function of a ranking process. | 05-05-2022 |
20220138631 | SYSTEMS AND METHODS FOR PHOTOVOLTAIC FAULT DETECTION USING A FEEDBACK-ENHANCED POSITIVE UNLABELED LEARNING - Various embodiments of a system and associated method for identifying and classifying faults in a photovoltaic array using relatively little labeled data are described herein. In particular, the system builds on existing PU classification techniques by addition of a feedback loop that enables classification of limited operational data of a photovoltaic array by expanding a plurality of features within the operational data based on a learned importance of each feature. | 05-05-2022 |
20220138633 | METHOD AND APPARATUS FOR INCREMENTAL LEARNING - An electronic device and method for performing class-incremental learning are provided. The method includes designating a pre-trained first model for at least one past data class as a first teacher; training a second model; designating the trained second model as a second teacher; performing dual-teacher information distillation by maximizing mutual information at intermediate layers of the first teacher and second teacher; and transferring the information to a combined student model. | 05-05-2022 |