Abstract: A predictive model reception unit 81 receives a predictive model that is learned based on an explained variable and an explanatory variable, indicates a relationship between the explained variable and the explanatory variable, and is represented by a function of the explanatory variable. An optimization unit 82 calculates, for an objective function having the received predictive model as an argument, an objective variable that optimizes the objective function, under a constraint condition.

Abstract: 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.

Abstract: An accuracy estimation unit 91 estimates accuracy of a predictive model using an accuracy estimating model that is learned using, as an explanatory variable, all or part of one or more contexts each indicating a feature value representing an operation status of the predictive model at a first point of interest that is a past point in time of interest a learning period of the predictive model, and a parameter used to learn the predictive model and, as a response variable, an accuracy index in a period after the first point of interest. The accuracy estimation unit 91 calculates the context at a second point of interest that is a point in time after the first point of interest, and applies the calculated context to the accuracy estimating model to estimate the accuracy from the second point of interest onward.

Abstract: A classifier 81 classifies 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 classifier 81 classifies 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.

Abstract: An explanatory variable display means 81 extracts an explanatory variable used as a condition from a classification model classified by the condition for selecting a component used for prediction and displays the explanatory variable in association with any of dimensional axes of a multi-dimensional space in which a prediction value is displayed. A prediction value display means 82 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, and then, displays the prediction value calculated on the basis of the specified component, on the same position. A space display means 83 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.

Abstract: A feature calculation unit 81 calculates a feature that is likely to influence cancellation by a user based on a communication state log that indicates a communication state of a base station when the user has been engaged in communication or making a call. A learning device 82 learns a model representing a behavioral characteristic of the user by using the calculated feature as an explanatory variable. A prediction device 83 predicts the behavioral characteristic of the user using the feature generated from the communication state log and the model.

Abstract: A prediction data input unit 91 inputs prediction data that is one or more explanatory variables that are information likely to affect future sales. An exposure pattern generation unit 92 generates 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 unit 93 determines 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.

Abstract: An estimation data input unit 90 inputs estimation data including one or more explanatory variables which are information that may influence deterioration of an object. A component determination unit 91 determines a component to be used for estimation of deterioration of the object based on a hierarchical latent structure, which is a structure in which latent variables are represented by a tree structure and each of the components representing a probability model is assigned to each node at the lowest level of the tree structure, a gate function to determine a branch direction at each node of the hierarchical latent structure, and the estimation data. A deterioration estimation unit 92 estimates the deterioration of the object based on the component determined by the component determination unit 91 and the estimation data.

Abstract: 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 system 1 displays 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.

Abstract: From learning data that expresses inter-node connection relationships that are expressed as a graph structure or a network structure, a vicinal node information acquisition unit 81 acquires edge information that indicates the connection relationship between one node and another node to which the one node connects. Using the acquired edge information and node feature information that indicates the features of the other node, a feature value calculation unit 82 calculates a feature value that is for the one node and that is to be used for prediction.

Abstract: An energy-amount estimation device that can predict an energy amount with a high degree of precision is disclosed. Said energy-amount estimation device has a prediction unit that, on the basis of the relationship between energy amount and one or more explanatory variables representing information that can influence said energy amount, predicts an energy amount pertaining to prediction information that indicates a prediction target. The aforementioned relationship is computed on the basis of specific learning information, within learning information in which an objective variable representing the aforementioned energy amount is associated with the one or more explanatory variables, that matches or is similar to the aforementioned prediction information.

Abstract: A feature-converting device that provides good features quickly. The device includes first and second feature construction units and first and second feature selection units. The first feature construction unit receives one or more first features and constructs one or more second features that represent the results of applying a unary function to the respective first features. The first feature selection unit computes relevance between the first and second features and a target variable that includes elements associated with elements included in the first features and selects one or more third features that represent highly relevant features. The second feature construction unit constructs one or more fourth features that represent the results of applying a multi-operand function to the third features. The second feature selection unit computes the relevance between the third and fourth features and the target variable and selects at least one fifth feature that represents highly relevant features.

Abstract: A price estimation device that can predict a price with a high degree of precision is disclosed. Said price estimation device has a price-predicting means that predicts a price pertaining to second information in a target second time period by applying rule information to said second information, which includes explanatory variables. Said rule information represents the relationship between the explanatory variables and the price, said relationship having been extracted on the basis of a first-information set comprising first information in which explanatory-variable values are associated with price values. The explanatory variables include an attribute that represents a length of time, determined on the basis of a first time period in which a specific event occurs, pertaining to a target object associated with the aforementioned first information or the abovementioned second information.

Abstract: 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.

Abstract: A model estimation device 100 includes a hidden variable variational probability calculation processing unit 104 for acquiring parameters of a hidden variable model and calculating a constrained hidden variable variational probability as a hidden variable posterior probability close to a previously-given distribution by use of the parameters, a model parameter optimization processing unit 105 for optimizing the parameters of the hidden variable model by use of the constrained hidden variable variational probability, and an optimality determination processing unit 106 for determining whether a marginalized log likelihood function using the optimized parameters is converged, wherein when it is determined that the marginalized log likelihood function is converged, the constrained hidden variable variational probability and the parameters used for the marginalized log likelihood function are output.

Abstract: This invention helps improve the precision of data mining. This information processing device is provided with the following: a function-defining means that defines a new function by composing a plurality of functions; an attribute-generating means that applies said new function to an attribute to generate a new attribute that is the result of applying that function to that attribute; and a determining means that inputs the new attribute to an analysis engine, which executes an analysis process on the basis of the attribute, and determines whether or not information outputted by said analysis engine satisfies a prescribed requirement.

Abstract: This invention helps improve the precision of data mining. This information processing system is provided with an attribute-generating means and an evaluating means, as follows. From among a plurality of inputted attributes, the attribute-generating means selects a combination of attributes to serve as operands for a function that defines an operation that takes a plurality of operands. The attribute-generating means applies said function to that combination of attributes to generate a new attribute that is the result of applying that function to that combination of attributes. The evaluating means inputs said new attribute to an analysis engine, which executes an analysis process on the basis of the attribute, and determines whether or not information outputted by said analysis engine satisfies a prescribed requirement.

Abstract: A model estimation device includes: a data input unit 101; 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.

Abstract: A hierarchical latent structure setting unit 81 sets 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. A variational probability computation unit 82 computes a variational probability of a path latent variable that is a latent variable included in a path linking a root node to a target node in the hierarchical latent structure. A component optimization unit 83 optimizes each of the components for the computed variational probability. A gating function optimization unit 84 optimizes a gating function model that is a model for determining a branch direction according to the multivariate data in a node of the hierarchical latent structure, based on the variational probability of the latent variable in the node.

Abstract: A gradient computation unit computes a gradient of an objective function in a variable to be optimized. An added variable selection unit adds a variable corresponding to a largest absolute value of the computed gradient from among variables included in a variable set, to a nonzero variable set. A variable optimization unit optimizes a value of the variable to be optimized, for each variable included in the nonzero variable set. A deleted variable selection unit deletes a variable that, when deleted, causes a smallest increase of the objective function from among variables included in the nonzero variable set, from the nonzero variable set. An objective function evaluation unit computes a value of the objective function for the variable to be optimized.