Abstract: An estimation results display system that, in the case of displaying an estimation result derived using a learning model, enables persons to recognize how condition determination is performed to select the learning model is provided. Input means 91 receives input of information associating information indicating a learning model selected depending on a determination result of whether or not an attribute in estimation data including one or more types of attributes satisfies one or more types of conditions and an estimation result derived using the learning model. Display means 92 displays the estimation result, in association with the information indicating the learning model used for deriving the estimation result and a condition subjected to determination of whether or not satisfied by the attribute in the estimation data when selecting the learning model.

Abstract: An information processing apparatus according to the present invention divides a period in which performance data at a business facility as a prediction target is present into a plurality of partial periods. The information processing apparatus performs prediction processing using each of a plurality of prediction models for a second partial period which is a partial period other than a first partial period including a start time of a predetermined period, and compares the result of the process with the performance data in a partial period as a target of the prediction processing. The information processing apparatus decides a prediction model to be used for sales prediction for a period subsequent to the predetermined period on the basis of the result of the comparison.

Abstract: An air traffic control support system for more quickly grasping a flight plan in air traffic control is provided. An air traffic control support system 3 includes a learning unit 100 and a prediction unit 200. The learning unit 100 generates a prediction model, based on learning data including a past flight plan and information that affected formulation of the past flight plan. The prediction unit 200 predicts a flight plan, based on information that affects formulation of the flight plan and a prediction model.

Abstract: Reception means 81 receives an estimator learned using measured data up to a point of time in the past, verification data that is measured data from the point of time onward, and an update rule prescribing whether or not the estimator needs to be updated based on an evaluation index. Simulation means 82 simulates at least one of the evaluation index of the estimator and an update result of the estimator in a predetermined period, based on the update rule and an estimation result calculated by applying the verification data of the predetermined period to the estimator in chronological order.

Abstract: A visualization system, in the case where a prediction target is expressed by a sum of a plurality of partial prediction targets, includes: a reception unit 81 for receiving designation of a method of compiling the partial prediction targets into a plurality of groups; a learning unit 82 for learning, for at least one of the groups, a model having, as an objective variable, a sum of partial prediction targets included in the group; and a first display control unit 83 for causing a display device to display parameters constituting the model.

Abstract: An information processing system 80 includes a storage unit 81 which stores a plurality of prediction models that are each identified by a plurality of classifications and used for predicting a value of a prediction target, a reception unit 82 which receives at least one of the plurality of classifications, and an extraction unit 83 which extracts a prediction model from the storage unit 81 based on the classification received by the reception unit 82.

Abstract: A storage unit 81 stores information associating each of a plurality of prediction targets with a predictor-related index related to a predictor for predicting the prediction target. Scatter graph generation means 82 generates, based on the information stored in the storage unit 81, a scatter graph in which a symbol representing the prediction target of the predictor is located at a position determined by the predictor-related index in a coordinate space where the predictor-related index is defined as at least one dimension.

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 information processing system 80 configured to predict a prediction target specified by a plurality of classifications using a prediction model including a variable that affects the prediction target, includes an accepting unit 81 and an aggregating unit 82. The accepting unit 81 accepts classifications that specify the prediction target. The aggregating unit 82 specifies the prediction target by the accepted classifications and aggregates, for each of the variables, a degree of contribution determined by the prediction model corresponding to that prediction target.

Abstract: An estimation results display system that, in the case of displaying an estimation result derived using a learning model, enables persons to recognize how condition determination is performed to select the learning model is provided. Input means 91 receives input of information associating information indicating a learning model selected depending on a determination result of whether or not an attribute in estimation data including one or more types of attributes satisfies one or more types of conditions and an estimation result derived using the learning model. Display means 92 displays the estimation result, in association with the information indicating the learning model used for deriving the estimation result and a condition subjected to determination of whether or not satisfied by the attribute in the estimation data when selecting the learning model.

Abstract: In this invention, a property of a prediction target or analysis target can be predicted or analyzed with a high degree of precision during a transition from a stage in which there is extremely little or no known data about said prediction target or analysis target to a stage in which a sufficient amount of known data has been accumulated. This learning-model selection system comprises a model-evaluating means for evaluating learning models and a model-selecting means for selecting either a target learning model or a higher-order learning model on the basis of the result of the evaluation.

Abstract: Predictive model evaluation means 81 evaluates closeness in property between a relearned predictive model and a pre-relearning predictive model. Predictive model updating means 82 updates the pre-relearning predictive model with the relearned predictive model, in the case where the closeness in property meets closeness prescribed by a predetermined condition. The predictive model evaluation means 81 evaluates closeness in prediction result or structural closeness, as the closeness in property of the predictive model.

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 storage unit 81 stores information associating each of a plurality of prediction targets with a predictor-related index related to a predictor for predicting the prediction target. Scatter graph generation means 82 generates, based on the information stored in the storage unit 81, a scatter graph in which a symbol representing the prediction target of the predictor is located at a position determined by the predictor-related index in a coordinate space where the predictor-related index is defined as at least one dimension.

Abstract: Provided is a learning model generation system capable of preventing a decrease in prediction accuracy in a case where the trend of an actual value of a prediction target has changed. The learning model generation means 71 generates a learning model using, as learning data, time series data in which a value of each explanatory variable used in prediction of a prediction target is associated with an actual value of the prediction target. The prediction means 72 calculates a predicted value of the prediction target using the learning model once the value of each explanatory variable is given. The change point determination means 73 determines a change point which is a point in time when a trend of the actual value of the prediction target changed.

Abstract: Reception means 81 receives an estimator learned using measured data up to a point of time in the past, verification data that is measured data from the point of time onward, and an update rule prescribing whether or not the estimator needs to be updated based on an evaluation index. Simulation means 82 simulates at least one of the evaluation index of the estimator and an update result of the estimator in a predetermined period, based on the update rule and an estimation result calculated by applying the verification data of the predetermined period to the estimator in chronological order.

Abstract: A predictor management system includes a storage unit 81 and update history management means 82. The storage unit 81 stores, in association with each of a plurality of prediction targets, an update history of a predictor corresponding to the prediction target. The update history management means 82 stores, in response to updating of a predictor, a prediction target of the predictor and an update time of the predictor in the storage unit 81 in association with each other.

Abstract: Provided is an information processing device to preferentially present information which each user does not have detailed knowledge, among inference results inferred from context. The information processing device includes: an inference unit that obtains inference results by applying inference rules to context information; an inference result index value calculation unit that calculates, on the basis of a knowledge level of a reading user about each inference rule used in an inference process, index values that show depth of knowledge of the reading user about the inference results comprehensively; an inference result presentation unit that presents the inference results on the basis of the index values; and an knowledge level update unit that updates the knowledge level of the reading user about each inference rule used in the inference process on the basis of evaluation information acquired.

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: In an impact visualization system that enables visualization of impacts of explanatory variables used in a prediction formula, the prediction formula being expressed by a linear sum of functions of the explanatory variables, an explanatory variable display unit 81 displays the explanatory variables used in the prediction formula on one dimensional axis by allocating a predetermined width to a respective one of the explanatory variables. A function value display unit 82 sets values or segments of the explanatory variables in the widths allocated thereto, in accordance with possible values or segments of the respective explanatory variables, and plots values of the functions specified by the values or the segments that have been set, at corresponding positions in another dimensional axis direction.