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 classification axis allocating means 81 is a visualizing device that visualizes a classification model in which classification conditions for classifying classification target data are represented in a hierarchical structure, and allocates classification axes to respective dimensional axes of a multidimensional space in accordance with priority levels of the classification axes to be used for the classification conditions. A region splitting means 82 splits the dimensional axes, based on types of the allocated classification axes, and allocates classification target data to respective regions on the multidimensional space to be split in accordance with the split dimensional axes. A display means 83 displays the classification target data allocated to the respective regions of the multidimensional space.

Abstract: Provided is an explanatory variable display priority order determination system that can determine a display priority order for explanatory variables provided for estimated value calculation and can display the explanatory variables. A display priority order determination means 73 determines the display priority order of the explanatory variables to be used in estimation of a value by an estimation formula on the basis of a residual as an estimation amount of an error between an estimated value by the estimation formula and a measured value. At this time, the display priority order determination means 73 determines the display priority order of the explanatory variables on the basis of magnitude of an influence of the explanatory variables on the residual. A display means 74 displays the explanatory variables according to the display priority order.

Abstract: An estimation results display system capable of displaying an estimation result so that persons can intuitively recognize at a glance which learning model is selected when deriving the estimation result is provided. Input means 2 receives input of information associating an estimation result and information indicating a learning model used when deriving the estimation result. Display means 3 displays a graph that represents the estimation result by a symbol and in which a type of the symbol is changed depending on the learning model corresponding to the estimation result.

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: Inference results for which each user does not have detailed knowledge are presented with priority. An information processing device 1 includes: an inference unit 104 that obtains inference results by applying inference rules to context information; an inference result index value calculation unit 105 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 106 that presents the inference results on the basis of the index values; and an knowledge level update unit 109 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: It is an object of the present invention to provide an information classification device capable of classifying retrieved pieces of information into appropriate groups even if these pieces of information are the same kind of information. The information classification device according to the present invention includes spatial arrangement means and classification means. The spatial arrangement means performs processing for spatially arranging an information group of a first information type and an information group of a second information type based on relation between the information group of the first information type and the information group of the second information type. The classification means classifies the information group of the first information type based on the processing results of the spatial arrangement means.