Nec Corporation

Dotdata, Inc.

Yusuke Muraoka

Yukitaka Kusumura

Ryohei Fujimaki

Hironori Mizuguchi

Dai Kusui

Masato Asahara

Riki Eto

Kazuyo Narita

Ting Chen

Sergey Tarasenko

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 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

The prediction function creation device according to the present invention for creating a prediction function to derive an objective variable by using a set of samples that include explanatory variables and an objective variable, the device includes: a clustering unit that clusters the respective samples by giving labels, and assigns weights to each label in accordance with patterns of missing values for the explanatory variables in labeled samples; a child model creation unit that makes portions of the training data partial training data on the basis of the weights, and determines an explanatory variable that constitutes the prediction function on the basis of patterns of missing values for the explanatory variables in the samples; and a mixture model creation unit that creates the prediction function with respect to each pattern of missing values by using the explanatory variable and the determined partial training data.

Prediction function creation device, prediction function creation method, and computer-readable storage medium

Facilitate the procedure of evaluating a predictor. This evaluation system comprises an input receiving unit via which elements constituting an evaluation index are specified and an evaluation-index calculation unit that calculates an evaluation-index value for a data set. The evaluation index comprises an element of a first type that evaluates the sample data, an element of a second type that applies weights to the sample data, and an element of a third type that performs a statistical process on a plurality of sample data based on information outputted by the element of the first type and the element of the second type. The evaluation-index calculation unit calculates the above-mentioned evaluation-index value based on the evaluation index comprising the elements received by the input receiving unit.

Evaluation system, evaluation method, and computer-readable storage medium

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

In an inverted list of each node in a taxonomy, among each node, an inverted list of the highest node is a list of integer values indicating an identifier of search subject data, and an inverted list of a node other than the highest node, in place of the identifier, is a list of integer values indicating a position in an inverted list corresponding to a node that is higher by one than the node. Furthermore, a list of integer values in an inverted list of each node is divided into two or more blocks, and a differential value between an integer value and an integer value directly before the integer value in the block is converted into a bit string of a variable length integer code.

Data structure, index creation device, data search device, index creation method, data search method, and computer-readable recording medium

Provided are a word latent topic estimation device and a word latent topic estimation method which are capable of hierarchically performing processing and which are capable of rapidly estimating latent topics of a word while taking into consideration a mixed state of topics. The present invention is provided with: a document data addition unit () which inputs a document which includes one or more words; a level setting unit () which sets a number of topics at each level in accordance with a hierarchical structure of topics for hierarchically estimating latent topics of a word; a higher-level constraint creation unit () which, on the basis of results of topic estimation at a given level with regard to a word within the document, creates a higher-level constraint indicating an identifier of a topic for which there is a possibility of being assigned to the word and a probability of being assigned to the topic; and a higher-level-constraint-attached topic estimation unit () which, when estimating the probability of each word being assigned to each topic, refers to the higher-order constraint, uses the probability of being assigned to a parent topic at the higher level as a weight, and performs estimation processing to a lower-level topic.

Word latent topic estimation device and word latent topic estimation method

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.

Model estimation device and model estimation method

In order to accurately learn a function for evaluating documents, even in the case where sample documents having missing feature values are included as training data, a document evaluation apparatus is provided with a data classification unit () that classifies a set of sample documents based on missing patterns of a first feature vector, a first learning unit () that uses feature values that are not missing in the first feature vector and evaluation values to learn a first function for calculating a first score which is a weighted evaluation value for each classification, a feature vector generation unit () that computes a feature value corresponding to each classification using the first score, and generates a second feature vector having the computed feature values, and a second learning unit () that uses the second feature vector and the evaluation values to learn a second function for calculating a second score for evaluating documents targeted for evaluation.

Document evaluation apparatus, document evaluation method, and computer-readable recording medium using missing patterns

A linkage information output apparatus includes: a linkage information retrieval unit for acquiring, upon receiving source information, destination information linked with the source information, a frequency of occurrence of the source information, a frequency of occurrence of linked each of the destination information, and a frequency of occurrence of a link of the source information and each of the destination information from a linkage information accumulation unit; a recognition degree calculation unit calculating, based on each acquired frequency of occurrence, a recognition degree of the source information, a recognition degree of each acquired destination information, and a recognition degree of each link; and a high interest information narrowing unit selecting destination information to output from among each destination information based on a combination of two or more among a recognition degree of the source information, a recognition degree of the destination information, and a recognition degree of the link.

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Tokyo, Japan

Yusuke Muraoka