Nec Corporation

Dotdata, Inc.

Nec C Orporation

Ryohei Fujimaki

Satoshi Morinaga

Yusuke Muraoka

Yukitaka Kusumura

Masato Asahara

Riki Eto

Hidenori Tsukahara

Yousuke Motohashi

Takayuki Nakata

Akinori Satou

Akihiro Yabe

Hironori Mizuguchi

Shinji Ito

Kazuyo Narita

Yuki Chiba

Ting Chen

Yoshinobu Kawahara

Shunsuke Hirose

Sergey Tarasenko

Yosuke Motohashi

Kyota Kanno

Ji Liu

Kouhei Hayashi

Syunsuke Hirose

Hidekazu Oiwa

A descriptor generation unituses a first template prepared in advance to generate a feature descriptor, which generates a feature that may affect a prediction target from a first table including a variable of the prediction target and a second table. A feature generation unitgenerates the feature by applying the feature descriptor to the first and second tables. A feature explanation generation unitgenerates a feature explanation about the feature descriptor or the feature on the basis of a second template. An accepting unitaccepts values to be assigned to the first and second templates. The descriptor generation unitgenerates the feature descriptor by assigning the accepted values to the first template, and the feature explanation generation unitgenerates the feature explanation by assigning the values assigned to the first template to the second template.

Information processing system, feature description method and feature description program

A distribution systemincludes a data management apparatusand a plurality of calculatorsthat execute machine learning. The data management apparatusincludes a data acquisition unitthat acquires information regarding training data held in a memoryof each of the calculators, from the calculators, and a data rearrangement unitthat determines training data that is to be held in the memoryof each of the calculators, based on characteristics of the machine learning processes that are executed by the calculators, and the information acquired from the calculators.

Distribution system, data management apparatus, data management method, and computer-readable recording medium

A learning unitgenerates a plurality of sample groups from samples to be used for learning, and generates a plurality of prediction models while inhibiting overlapping of a sample group to be used for learning among the generated sample groups. An optimization unitgenerates an objective function based on an explained variable predicted by the prediction model and based on a constraint condition for optimization, and optimizes a generated objective function. An evaluation unitevaluates an optimization result by using a sample group that has not been used in learning of a prediction model used for generating an objective function targeted for the optimization.

Evaluation system, evaluation method, and evaluation program for evaluating a result of optimization based on prediction

A table acquiring meansacquires a first table including prediction objects and first attributes, and a second table including second attributes. A receiving meansreceives a similarity function and condition for similarity used to calculate the similarity between the first attribute and the second attribute. A feature generating meansgenerates feature candidates able to affect a prediction object using a combination condition for combining a record in the first table including the value of a first attribute satisfying the condition with a record in the second table including the value of a second attribute satisfying the similarity calculated with the value of the first attribute and the value of the second attribute using the similarity function, and using a reduction method for a plurality of records in the second table and a reduction condition represented by the column to be aggregated. A feature selecting meansselects an optimum feature for the prediction from the feature candidates.

Feature value generation device, feature value generation method, and feature value generation program

A computational resource management apparatus is for managing a cluster system that executes a plurality of tasks. The computational resource management apparatus includes a condition specification unit that specifies a relationship between computational resources of the cluster system and computation time, a dependency relationship between tasks, and an execution time limit of each task, and a scheduling unit that determines, for each task, an execution sequence and computational resources to be allocated from among the computational resources of the cluster system, based on the relationship between the computational resources and computation time and the dependency relationship that are specified, such that the execution time limit is met.

Computational resource management apparatus which optimizes computational resources to be allocated to tasks having a dependency relationship, computational resource management method and computer readable recording medium

A prediction data input unitinputs prediction data that is one or more explanatory variables that are information likely to affect future sales. An exposure pattern generation unitgenerates an exposure pattern which is an explanatory variable indicating the content of a commercial message scheduled to be performed during a period from predicted time to future prediction target time. A component determination unitdetermines the component used for predicting the sales, on the basis of a hierarchical latent structure that is a structure in which latent variables are represented by a tree structure and components representing probability models are located at nodes of a lowest level of the tree structure, gating functions for determining a branch direction in the nodes of the hierarchical latent structure, and the prediction data and the exposure pattern. A sales prediction unitpredicts the sales on the basis of the component determined by the component determination unitand of the prediction data and the exposure pattern.

Commercial message planning assistance system and sales prediction assistance system

A computational resource management device uses a measured value of an execution time of data processing, a measured value of a resource amount, and a feature of input data as training data to learn a model indicating a relationship between the execution time and the resource. The device inputs, into the model, a feature of data scheduled to be input to calculate an estimated value of the execution time of the scheduled data processing, and uses the estimated value of the execution time, a variation index indicating variation in the estimated value of the execution time, and distribution of estimated residuals to calculate a resource amount required in the scheduled data processing. The device creates an execution plan of the scheduled data processing, based on the estimated value of the execution time, the variation index, the distribution of estimated residuals, and the calculated resource amount.

Computational resource management device, computational resource management method, and computer-readable recording medium

An apparatus that extracts an explanatory variable used as a condition from a classification model classified by the condition for selecting a component used for prediction, displays the explanatory variable in association with any of dimensional axes of a multi-dimensional space in which a prediction value is displayed, specifies the component that corresponds to a position in the multi-dimensional space specified by each of the explanatory variables associated with the dimensional axis, displays the prediction value calculated based on the specified component, on the same position and displays the multi-dimensional space that corresponds to the position in which the prediction value is displayed, in a mode that corresponds to the component used for calculating the prediction value.

Prediction result display system, prediction result display method, and prediction result display program

A table storage unitstores a first table including an objective variable and a second table different in granularity from the first table. A descriptor creation unitcreates a feature descriptor for generating a feature which is a variable that can influence the objective variable, from the first table and the second table. The descriptor creation unitcreates a plurality of feature descriptors, each by generating a combination of a mapping condition element indicating a mapping condition for rows in the first table and the second table and a reduction method element indicating a reduction method for reducing, for each objective variable, data of each column included in the second table.

Information processing system, descriptor creation method, and descriptor creation program

A classifierclassifies target data into a cluster on the basis of a mixture model defined using two different types of variables that indicate features of the target data. In this classification, the classifierclassifies the target data into a cluster on the basis of a mixture model in which a mixing ratio of the mixture model is represented by a function of a first variable and in which the element distribution of the clusters into which the target data is classified is represented by a function of a second variable.

Clustering system, method, and program

A region linear model optimization system optimizes a region linear model, and includes: a linear model setting unitfor setting for a partition a linear model to be applied to one of regions representing subspaces divided by the partition, the partition being an indicator function dividing an input space into two portions; and a region model calculation unitfor representing a model of each of the regions in the region linear model as a linear combination of the linear models to be applied to the respective regions.

Region linear model optimization system, method and program

Provided is a feature-value display system which can display a feature value of a node for accurate prediction of a state of the node in a graph structure or a network structure. The feature-value display systemdisplays the feature value of the current node, considering information generated on the basis of attribute information associated with the nodes adjacent to or closer to a current node in the graph structure or the network structure, as the feature value of the current node itself.

Feature-value display system, feature-value display method, and feature-value display program

This invention provides a water-leakage state estimation system configured to estimate a state of a water leakage in a specific area of a water distribution network. A learning unit is configured to: receive labeled data, which is labeled so as to separate past flow rate data into abnormal values and normal values, and past environment state condition data; build a prediction model for predicting the normal values in the labeled data through learning; and determine a score parameter defining a length of a period involving data to be verified through learning as well. A water-leakage estimation unit is configured to: compare predicted flow rate data obtained by supplying current environment condition data into the prediction model and current flow rate data to produce error values; and calculate an average value of the error values in the period of a window width defined by the score parameter to estimate a water-leakage score representing a state of the water-leakage in the specific area.

Water-leak state estimation system, method, and recording medium

A model estimation device includes: a data input unit; a state number setting unit; an initialization unit which sets initial values of a variational probability of a latent variable, a parameter, and the type of each component; a latent variable variational probability computation unit which computes the variational probability of the latent variable so as to maximize a lower bound of a marginal model posterior probability; a component optimization unit which estimates an optimal type of each component and a parameter thereof so as to maximize the lower bound of the marginal model posterior probability separated for each component of the latent variable model; an optimality determination unit which determines whether or not to continue the maximization of the lower bound of the marginal model posterior probability; and a result output unit which outputs a result.

Model estimation device, model estimation method, and information storage medium

A model estimation device includes: a data input unit; a state number setting unit; an initialization unit; a latent variable variational probability computation unit which computes a variational probability of a latent variable so as to maximize a lower bound of a model posterior probability limited in degree of freedom; a component optimization unit which estimates an optimal type of each component and a parameter thereof so as to maximize the lower bound of the model posterior probability limited in degree of freedom and separated for each component of a latent variable model; a free parameter selection variable computation unit which computes the free parameter selection variable; an optimality determination unit which determines whether or not to continue the maximization of the lower bound of the model posterior probability; and a result output unit.

Growing community of inventors

Tokyo, Japan

Ryohei Fujimaki