Abstract: A control device according to one embodiment includes control means that selects an action at for controlling a people flow in accordance with a measure ? at each control step “t” of an agent in A2C by using a state st obtained by observation of a traffic condition about the people flow in a simulator and learning means that learns a parameter of a neural network which realizes an advantage function expressed by an action value function representing a value of selection of the action at in the state st under the measure ? and by a state value function representing a value of the state st under the measure ?.

Abstract: A training apparatus includes a calculation unit that takes aggregate data obtained by aggregating history data representing a history of second objects for each first object from a predetermined viewpoint, auxiliary data representing auxiliary information regarding the second object, and partial history data that is a part of the history data as inputs and calculates a value of a predetermined objective function, which represents a degree of matching between co-occurrence information representing a co-occurrence relationship of two second objects, and the aggregate data, the auxiliary data, and the partial history data, and a derivative of the objective function with respect to a parameter, and an updating unit that updates the parameter such that the value of the objective function is maximized or minimized using the value of the objective function and the derivative calculated by the calculation unit.

Abstract: A learning device includes a learning unit that learns parameters for determining an occurrence probability of an event at each time and each location on the basis of history information relating to the event, the history information including a time, a location, and an event type, and features of an area corresponding to the location, so that a likelihood expressing a combined effect of the event type and the features of the area on the event is optimized.

Abstract: A parameter estimation section 106 is configured to perform estimation, for aggregate data in which values are associated with respective regions obtained by subdividing a space and for a Gaussian process model that expresses a plurality of aggregate data of differing partition granularity. The estimation is performed based on the Gaussian process model including a spatial scale parameter of a correlation function between regions of the aggregate data and including a noise variance parameter of the correlation function, by estimating the spatial scale parameter and the noise variance parameter so as to maximize a function expressing values of the aggregate data by area integrals of a Gaussian process.

Abstract: A training device (10) includes a training data input unit (11) that accepts input of labeled data of a source domain and/or unlabeled data of a source domain as training data, a feature extraction unit (12) that converts data unique to each source domain of which input has been accepted by the training data input unit (11), to a feature vector, and a training unit (13) that trains a predictor (141) that performs data embedding suited to an input domain, in accordance with metric learning by using the feature vector of each source domain.

Abstract: A training apparatus includes a calculation unit that takes a set of first data elements that are labeled and a set of second data elements that are unlabeled as inputs and calculates a value of a predetermined objective function that represents an evaluation index when a false positive rate is in a predetermined range and a derivative of the objective function with respect to a parameter, and an updating unit that updates the parameter such that the value of the objective function is maximized or minimized using the value of the objective function and the derivative calculated by the calculation unit.

Abstract: A learning apparatus includes: an input unit configured to input a first data set constituted by data indicative of being normal and a second data set constituted by a collection of data sets including at least one piece of data indicative of being anomalous; a calculation unit configured to calculate, using data included in the first data set and data included in the second data set, a value of an objective function utilizing a model and a derivative value of the objective function regarding a parameter of the model, the model estimating an anomaly score of data; and an updating unit configured to update, using the value of the objective function and the derivative value of the objective function, the parameter of the model.

Abstract: A learning apparatus includes an input unit configured to input route information indicating a route including one or more paths of a plurality of paths and moving body number information indicating the number of moving bodies for a date and a time on a path to be observed among the plurality of paths, and a learning unit configured to learn a parameter of a model indicating a relationship between the number of moving bodies for each of the plurality of paths and the number of moving bodies for the route and a relationship between the numbers of moving bodies for the route at different dates and times by using the route information and the moving body number information.

Abstract: Included are a vector estimation means that estimates, in response to receiving input of a plurality of pieces of relational data each including a plurality of objects and a relationship between the objects, for each piece of relational data, a latent vector for characterizing a structure of the relational data by using the objects and the relationship included in the relational data; and a matching means that matches, for each set of first relational data and second relational data different from each other of the received pieces of relational data, a first object and a second object by using a first latent vector corresponding to the first object included in the first relational data and a second latent vector corresponding to the second object included in the second relational data.

Abstract: An estimation apparatus includes an input unit configured to input data related to a plurality of optimization problems, and an estimation unit configured to estimate a parameter of a function model that models a function to be optimized in each of the plurality of optimization problems. Additionally, the optimization apparatus includes an input unit configured to input a function model that models a function to be optimized in each of a plurality of optimization problems, and an optimization unit configured to optimize a target function by repeatedly evaluating the target function to be optimized in an optimization problem different from each of the plurality of optimization problems, using the function model.

Abstract: A learning device includes: input means for inputting route information on a set of routes each constituted by one or more ways, and passing mobile object information that indicates the number of passing mobile objects on an observed way, out of the one or more ways, at each time point; and learning means for learning parameters of a model in which a travel speed of the mobile objects is taken into consideration, using the route information and the passing mobile object information.

Abstract: A learning apparatus includes an input data reading unit configured to input data and a label indicating whether the data is abnormal, an objective function calculation unit configured to calculate a value of an objective function based on the label and a predetermined function for calculating an anomaly score of the data by applying a parameter relating to the anomaly score, by using the data and a value of the parameter, and a parameter update unit configured to calculate a value of the parameter that maximizes the value of the objective function by repeatedly executing a process by the objective function calculation unit while updating the value of the parameter.

Abstract: To create a classifier whose classification accuracy is maintained in consideration of temporal changes in a generation distribution of a sample and a new feature that has not appeared in learning data, a classifier is created in which a feature correlation learning unit learns a correlation between a feature of a sample of labeled learning data, and a feature appearing only in a sample of unlabeled learning data, and a classifier creating unit adds the feature appearing only in the sample of the unlabeled learning data to the feature of the sample of the labeled learning data by using the correlation, and outputs a label associated with an input sample by using the sample of the labeled learning data to which the feature appearing only in the sample of the unlabeled learning data is added.

Abstract: A learning device (10) receives an input of labeled data of a plurality of source domains relevant to a target domain and learns a supervised model predictor using information unique to each domain in the labeled data of the plurality of source domains. Further, the prediction device (20) receives an input of unlabeled data of a target domain, outputs a supervised model suitable for the target domain using the learned supervised model predictor, performs prediction of the unlabeled data of the target domain using the supervised model, and output a prediction result.

Abstract: Included are a vector estimation means that estimates, in response to receiving input of a plurality of pieces of relational data each including a plurality of objects and a relationship between the objects, for each piece of relational data, a latent vector for characterizing a structure of the relational data by using the objects and the relationship included in the relational data; and a matching means that matches, for each set of first relational data and second relational data different from each other of the received pieces of relational data, a first object and a second object by using a first latent vector corresponding to the first object included in the first relational data and a second latent vector corresponding to the second object included in the second relational data.

Abstract: An acquisition unit (15a) acquires data output by sensors. A learning unit (15b) substitutes a prior distribution of an encoder in a generative model including the encoder and a decoder and representing a probability distribution of the data with a marginalized posterior distribution that marginalizes the encoder, approximates a Kullback-Leibler information quantity using a density ratio between a standard Gaussian distribution and the marginalized posterior distribution, and learns the generative model using data. A detection unit (15c) estimates a probability distribution of the data using the learned generative model and detects an event in that an estimated occurrence probability of the data newly acquired is lower than a prescribed threshold as abnormality.

Abstract: Disclosed is a method whereby a solution of an optimization problem under multiple structures can be obtained at high speed even when a function to be minimized is ill-conditioned.

Abstract: A learning section (13) learns a classification criterion of a classifier at each time point using labeled learning data collected until a past prescribed time point and unlabeled learning data collected on and after the prescribed time point and learns a time-series change of the classification criterion. A classifier creation section (14) predicts a classification criterion of the classifier at an arbitrary time point including a future time point and certainty expressing the reliability of the classification criterion using the learned classification criterion and the time-series change. Thus, the classifier that outputs a label expressing an attribute of input data is created.

Abstract: The present disclosure relates to an apparatus and methods of estimating a traffic volume of moving objects. In particular, the present disclosure estimates the traffic volume based on amounts of traffic volume of the moving objects observed at observation points based on a routing matrix and a visitor matrix. The routing matrix indicates whether moving objects that pass through specific waypoints are to be observed at an observation point. The visitor matrix indicates whether a moving object departing or arriving at the observation point. The present disclosure enables estimating a traffic volume of moving objects on various routes based on observed data with errors in data and varying lengths in observation periods.

Abstract: To create a classifier whose classification accuracy is maintained in consideration of temporal changes in a generation distribution of a sample and a new feature that has not appeared in learning data, a classifier is created in which a feature correlation learning unit learns a correlation between a feature of a sample of labeled learning data, and a feature appearing only in a sample of unlabeled learning data, and a classifier creating unit adds the feature appearing only in the sample of the unlabeled learning data to the feature of the sample of the labeled learning data by using the correlation, and outputs a label associated with an input sample by using the sample of the labeled learning data to which the feature appearing only in the sample of the unlabeled learning data is added.