Patent application number | Description | Published |
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 |
20110268364 | METHOD, PROGRAM, AND SYSTEM FOR CALCULATING SIMILARITY BETWEEN NODES IN GRAPH - A computer-implemented method, program, and system for calculating similarity between nodes in a graph by computer processing. The method includes: calculating a new label value of a node on the basis of a label value of a node adjacent to the node with respect to each of the nodes in one or more graphs; correcting the new label value of the adjacent node to remove an influence of the label value of a target node with respect to each of the target nodes for the calculation of the similarity between the nodes; and calculating the similarity between the target nodes by using the corrected new label value of the node adjacent to one target node and the corrected new label value of the node adjacent to another target node. | 11-03-2011 |
20120093417 | GRAPH SIMILARITY CALCULATION SYSTEM, METHOD AND PROGRAM - A computer implemented method and system for calculating a degree of similarity between two graphs whose nodes are respectively given discrete labels include providing, for each of the two graphs, label values respectively to a given node and nodes adjacent thereto so that different ones of the discrete labels correspond to different ones of the label values. The nodes are sequentially tracing for each of the two graphs, and, during the tracing of the nodes, a new label value is calculated through a hash calculation using a label value of a currently visited node and also using label values of nodes adjacent to the currently visited node to update the label value to the currently visited node. The degree of similarity between the two graphs is calculated on the basis of the number of the label values having been given to nodes of the two graphs and agreeing between the two graphs. | 04-19-2012 |
20120143814 | LOCATION ESTIMATION SYSTEM, METHOD AND PROGRAM - Location estimation systems, methods, and non-transitory computer program products. The system includes: storage means provided in the computer, means for storing the vector datasets in the storage means of the computer, means for calculating the similarity between the vector dataset without any location label and each neighboring vector dataset with a location label, by using any one of a q-norm where 0≦q≦1 and an exponential attenuation function, and means for estimating the location label of the vector data without any location label from the calculated similarities. | 06-07-2012 |
20130116991 | TIME SERIES DATA ANALYSIS METHOD, SYSTEM AND COMPUTER PROGRAM - A method includes selecting, with a computer, a time lag that is the time delay until an explanatory variable time sequence applies an effect on a target variable time series, and a time window that is the time period for the explanatory variable time series to apply the impact on the target variable time series; converting, based upon the explanatory variable time series, to a cumulative time series structured by the cumulative values of each variable from each time point corresponding to a certain finite time; and solving the cumulative time series as an optimized problem introducing a regularization term, to obtain the value of the time lag and the value of the time window from the solved weight. | 05-09-2013 |
20130116992 | TIME SERIES DATA ANALYSIS METHOD, SYSTEM AND COMPUTER PROGRAM - A method includes selecting, with a computer, a time lag that is the time delay until an explanatory variable time sequence applies an effect on a target variable time series, and a time window that is the time period for the explanatory variable time series to apply the impact on the target variable time series; converting, based upon the explanatory variable time series, to a cumulative time series structured by the cumulative values of each variable from each time point corresponding to a certain finite time; and solving the cumulative time series as an optimized problem introducing a regularization term, to obtain the value of the time lag and the value of the time window from the solved weight. | 05-09-2013 |
20130274899 | METHOD, COMPUTER PROGRAM, AND COMPUTER FOR DETERMINING SYSTEM SITUATION - A method applied to a computer that determines a situation of a system includes the steps of: receiving measurement data from each of a plurality of measurement targets in the system; computing a plurality of sets of anomaly values based on the measurement data and a predetermined computation algorithm according to a plurality of classifications corresponding to a plurality of properties of each measurement target; and determining the situation of the system based on the sets of anomaly values and a predetermined determination algorithm. | 10-17-2013 |
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 |