Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A method and apparatus for data processing. The present invention provides a data processing apparatus that includes: a series acquisition section for acquiring a data series in which multiple pieces of data are arranged; a fragmentation section for fragmenting the data series to obtain multiple partial data series; a pattern extraction section for extracting multiple patterns of one or more pieces of data appearing in at least one of the multiple partial data series; and a generation section for generating a feature vector having element values, which vary according to whether to include each of the multiple patterns, for each of the multiple partial data series, respectively. There is also provided a method for data processing. The present invention allows for the generation of a feature vector from time-series data indicating a phenomenon the occurrence time of which is temporally irregular to detect features.

Abstract: A computer performs searching in order to optimize a plurality of input parameters. Each of the input parameters is input to a time-series trial process. The computer receives a plurality of input parameters and performs a trial process on each of the plurality of input parameters. The computer then calculates an evaluation value of the trial process performed on each of the plurality of input parameters and calculates a degree of similarity among a plurality of trial processes based on a feature value. Each of the feature values is extracted from the trial process performed on a corresponding one of the plurality of input parameters. The computer updates the plurality of input parameters based on the evaluation value of the trial process calculated for each of the plurality of input parameters and the degree of similarity among the plurality of trial processes.

Abstract: 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.

Abstract: A method for predicting an output variable from explanatory values provided as sets of combinations of discrete variables and continuous variables includes receiving input data that contains the explanatory variables to predict the output variable; searching for each element in the combinations for elements in a plurality of sets with matching discrete variables using training data which the output variable has been observed; applying a function giving the degree of similarity between two sets weighed by a scale variable to each element in the input data, and to one or more elements found in the elements of the input data to calculate function values, and calculating the sum of the function values for all of the elements in the input data; and applying the calculated sum for each element to a prediction equation for predicting the output variable to calculate a prediction value of the output variable for each element.

Abstract: Embodiments relate to updating a parameter defining a policy under a Markov decision process system environment. An aspect includes updating the policy parameter stored in a storage section of a controller according to an update equation. The update equation includes a term for decreasing a weighted sum of expected hitting times over a first state (s) and a second state (s?) of a statistic on the number of steps required to make a first state transition from the first state (s) to the second state (s?).

Abstract: A method, apparatus and computer program for detecting occurrence of an anomaly. The method can exclude arbitrariness and objectively judge whether a variation of a physical quantity to be detected is abnormal or not even when an external environment is fluctuating. The method includes acquiring multiple primary measurement values from a measurement target. Further, calculating and a reference value for each of the multiple primary measurement values by optimal learning. The method further includes calculating a relationship matrix which indicates mutual relationships between the multiple secondary measurement values. Further the method includes calculating an anomaly score for each of the secondary measurement value which indicates the degree of the measurement target being abnormal. The anomaly score is calculated by comparing the secondary measurement value with a predictive value which is calculated based on the relationship matrix and other secondary measurement values.

Abstract: Embodiments relate to updating a parameter defining a policy under a Markov decision process system environment. An aspect includes updating the policy parameter stored in a storage section of a controller according to an update equation. The update equation includes a term for decreasing a weighted sum of expected hitting times over a first state (s) and a second state (s?) of a statistic on the number of steps required to make a first state transition from the first state (s) to the second state (s?).

Abstract: 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.

Abstract: A system and method for determining an optimal action in consideration of risk. The method includes the steps of: (a) selecting a state from possible states in a current term; (b) selecting an action from action candidates that can be executed in a selected state; (c) calculating a probability distribution of an evaluation value for a selected action; (d) calculating a risk measure using the probability distribution of the evaluation value; (e) determining a weighting function conforming to at least one preference by taking the risk measure into consideration; (f) calculating a value measure of the selected action; (g) repeating steps (b) through (f) for all other the action candidates that can be executed in the selected state; and (h) comparing the value measures of the selected actions in order to determine an optimal action for the selected state.

Abstract: A method providing an analytical technique introducing label information into an anomaly detection model. Effective utilization of label information is based on introducing the degree of similarity between samples. Assuming, for example, there is a degree of similarity between normally labeled samples and no similarity between normally labeled and abnormally labeled samples. Also each sensor value is generated by the linear sum of a latent variable and a coefficient vector specific to each sensor. However, the magnitude of observation noise is formulated to vary according to the label information for the sensor values, and set so that normal label unlabeled anomalously labeled. A graph Laplacian is created based on the degree of similarity between samples, and determines the optimal linear transformation matrix according to a gradient method. A optimal linear transformation matrix is used to calculate an anomaly score for each sensor in the test samples.

Abstract: Embodiments relate to updating a parameter defining a policy under a Markov decision process system environment. An aspect includes updating the policy parameter stored in a storage section of a controller according to an update equation. The update equation includes a term for decreasing a weighted sum of expected hitting times over a first state (s) and a second state (s?) of a statistic on the number of steps required to make a first state transition from the first state (s) to the second state (s?).

Abstract: Embodiments relate to updating a parameter defining a policy under a Markov decision process system environment. An aspect includes updating the policy parameter stored in a storage section of a controller according to an update equation. The update equation includes a term for decreasing a weighted sum of expected hitting times over a first state (s) and a second state (s?) of a statistic on the number of steps required to make a first state transition from the first state (s) to the second state (s?).

Abstract: A system and method for determining an optimal action in consideration of risk. The method includes the steps of: (a) selecting a state from possible states in a current term; (b) selecting an action from action candidates that can be executed in a selected state; (c) calculating a probability distribution of an evaluation value for a selected action; (d) calculating a risk measure using the probability distribution of the evaluation value; (e) determining a weighting function conforming to at least one preference by taking the risk measure into consideration; (f) calculating a value measure of the selected action; (g) repeating steps (b) through (f) for all other the action candidates that can be executed in the selected state; and (h) comparing the value measures of the selected actions in order to determine an optimal action for the selected state.