Abstract: A feature design unit 81 designs, from relational data, a feature as a variable likely to affect an objective variable. A feature generating unit 82 generates the designed feature, from the relational data. A learning unit 83 learns a prediction model, on the basis of the generated feature.

Abstract: A region linear model optimization system optimizes a region linear model, and includes: a linear model setting unit 81 for 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 unit 82 for 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.

Abstract: A parameter estimation unit 81 estimates parameters of a neural network model that maximize the lower limit of a log marginal likelihood related to observation value data and hidden layer nodes. A variational probability estimation unit 82 estimates parameters of the variational probability of nodes that maximize the lower limit of the log marginal likelihood. A node deletion determination unit 83 determines nodes to be deleted on the basis of the variational probability of which the parameters have been estimated, and deletes nodes determined to correspond to the nodes to be deleted. A convergence determination unit 84 determines the convergence of the neural network model on the basis of the change in the variational probability.

Abstract: A feature selection unit 81 selects, from a set of features that can influence the sales volume of a product, a first feature set as a set of features that influence the sales volume and a second feature set as a set of features that influence a price of the product. A learning unit 82 learns a predictive model in which features included in the first feature set and the second feature set are set as explanatory variables, and the sales volume is set as a prediction target. An optimization unit 83 optimizes the price of the product under constraint conditions to increase a sales revenue defined by using the predictive model as an argument. Further, the learning unit 82 learns a predictive model in which at least one feature included in the second feature set but not included in the first feature set is set as an explanatory variable.

Abstract: An evaluation system 80 includes an evaluation unit 81 for evaluating, when there is a prediction model estimated using data generated from the true model, the optimal solution calculated from the prediction model in consideration of bias generated between evaluation based on the prediction model and evaluation based on the true model.

Abstract: A multi-task relationship learning system 80 for simultaneously estimating a plurality of prediction models includes a learner 81 for optimizing the prediction models so as to minimize a function that includes a sum total of errors indicating consistency with data and a regularization term deriving sparsity relating to differences between the prediction models, to estimate the prediction models.

Abstract: An accepting unit 81 accepts an optimization problem that can be formulated as BQP represented by zTAz+bTz by use of an n×n square matrix A and an n-dimensional vector b. A condition storage unit 82 stores characteristic conditions representing characteristics of a positive weighted directed graph. An optimization unit 83 transforms the optimization problem based on the characteristic conditions, and solves the accepted optimization problem by solving the transformed problem as a minimum cut problem of a network flow.

Abstract: Computational resource management device includes a model learning unit that uses a measured value of an execution time of data processing, a measured value of a deresource amount, and a feature of input data as training data to learn a model indicating relationship between the execution time and the resource, an execution time estimation unit that 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, a resource amount calculation unit that uses the estimated value, a variation index indicating variation in the estimated value, and distribution of estimated residuals to calculate resource amount required in the scheduled data processing, and an execution plan creation unit that creates an execution plan of the scheduled data processing, based on the estimated value, the variation index, the distribution of estimated residuals, and the calculated resource amount.

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: 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 optimization system according to the present invention includes: a memory; and one processor being coupled to the memory and accepting an indicator probabilistically indicating a range of a prediction error related to a predicted value of the sales quantity, the predicted value being calculated with the prediction formula when a prediction formula predicting a sales quantity of a commodity is expressed by a function of a price of the commodity; optimizing the price to maximize the sales amount acquired by the objective function under a constraint with an objective function acquiring a sales amount including and being determined by the sales quantity and the price; and taking the predicted value and optimizing the price to increase a minimum value of the sales amount within the range of the prediction error, the range being indicated by the indicator.

Abstract: An information processing device according to one aspect of the present invention includes: a memory; and at least one processor coupled to the memory wherein, the processor performing operation, the operation comprising: acquiring an optimization model for calculating an optimum solution considering variation in one or more parameters; calculating the optimum solution in the optimization model; transforming the optimization model based on the optimum solution; and outputting the optimum solution.

Abstract: A table storage unit 81 stores a first table including an objective variable and a second table different in granularity from the first table. A descriptor creation unit 82 creates 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 unit 82 creates 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.

Abstract: A region linear model optimization system optimizes a region linear model, and includes: a linear model setting unit 81 for 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 unit 82 for 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.

Abstract: Provided is an information processing system which perform suitable optimization even if there are input data not observed in mathematical optimization. A learning unit 71 learns a predictive model on the basis of an explained variable and explanatory variables, the predictive model representing a relationship between explained variable and explanatory variables and being expressed by a function of the explanatory variables. A visualization unit 72 visualizes the predictive model. When receiving the operation from the user, an optimization unit 73 calculates an objective variable optimizing an objective function under constraints, the objective function using, as an argument, a predictive model visualized by the visualization unit 72.

Abstract: An initial value determination means 71 determines an initial value of a scheduling parameter of a target system. Furthermore, a convergence determination means 75 determines whether the value of a predetermined evaluation function has converged. Until it is determined that the value of the predetermined evaluation function has converged, a state variable calculation means 72 repeatedly calculates a value of a state variable, a regression coefficient calculation means 73 repeatedly calculates a value of a regression coefficient, and a scheduling parameter prediction model derivation means repeatedly derives a scheduling parameter prediction model and calculates the value of the scheduling parameter. When the value of the predetermined evaluation function converges, a model estimation means 76 estimates a linear parameter-varying model of the target system on the basis of the value of the state variable and the value of the scheduling parameter at that point in time.

Abstract: A model input unit 84 receives a linear regression model represented by a function having an objective variable as an explanatory variable. A candidate point input unit 85 receives, for the objective variable included in the linear regression model, at least one candidate point which is a discrete candidate for a possible value of the objective variable. An optimization unit 86 calculates the objective variable that optimizes an objective function having the linear regression model as an argument.

Abstract: An information processing apparatus 100 includes: a reception unit 10 that receives analysis subjects and variables relating to the analysis subjects; and a graph generation unit 20 that specifies degrees of influence that the variables have on the analysis subjects with the degrees of influence divided into positive and negative, and generates a graph indicating the specified degrees of positive influence and the specified degrees of negative influence as distances between the variables and the analysis subjects.

Abstract: An information processing device according to the present invention includes: a problem generator that, based on a first optimization problem, a lower-dimensional expression that is an expression for approximating uncertain data for the first optimization problem at a lower dimension than a dimension of the uncertain data, and a first data region that is a region of the uncertain data, generates a second optimization problem into that the first optimization problem is transformed in such a way that the second optimization problem relates to the lower-dimensional expression, and a second data region into that the first data region is transformed; and a problem solver that computes an optimum solution to the second optimization problem by using the second data region.

Abstract: Provided is a user information estimation system capable of estimating demographic information about a user of a prepaid mobile terminal. An estimation model generation means 21 generates, on the basis of information relating to a mobile terminal in which demographic information about a user is known, and the demographic information, an estimation model with demographic information as an objective variable, and information relating to a mobile terminal as an explanatory variable. An estimation means 22 applies information relating to a prepaid mobile terminal to the estimation model, to calculate an estimated value of demographic information about a user of the prepaid mobile terminal.