Nippon Telegraph and Telephone Corporation

Tomoharu Iwata

Hitoshi Shimizu

Atsutoshi Kumagai

Yusuke Tanaka

Hiroshi Sawada

Tatsushi Matsubayashi

Takuma Otsuka

Naoki Marumo

Naonori Ueda

An estimation apparatus includes: input means for inputting aggregated data in which a plurality of data are aggregated, and feature data representing a feature of the aggregated data; determination means for determining a parameter of a model of the plurality of data before the aggregation of the aggregated data, using a predetermined function and the feature data; and estimation means for estimating a parameter of the function and the plurality of data by optimizing a predetermined objective function, using the aggregated data.

Estimation apparatus, estimation method and program to estimate function parameters and data values

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.

Learning apparatus, estimation apparatus, learning method, estimation method, and program

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.

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.

Estimation apparatus, estimation method and program

In a classifier whose classification accuracy is maintained without frequently collecting labeled learning data, a learning unit learns a classification criterion of a classifier at each time point in the past until the present and learns a time series change of the classification criterion by using data for learning to which a label is given and that is collected until the present. A classifier creating unit predicts a classification criterion of a future classifier and creates a classifier that outputs a label representing an attribute of input data by using the learned classification criterion and time series change.

Creating apparatus, creating method, and creating program

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. The vector estimation means estimates, when the relationship indicates a close relationship between a plurality of objects to each other, latent vectors corresponding to the objects such that the latent vectors corresponding to the respective objects have a close relationship to each other.

Analysis apparatus, analysis method and program

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.

Growing community of inventors

Soraku-gun, Japan

Tomoharu Iwata