Abstract: An information processing apparatus of the present disclosure includes: a region dividing unit that divides an instance input space of each of a plurality of machine learning models into a plurality of regions and assigns a probability to each of the division regions; a probability calculating unit that calculates a sampling probability on a predetermined instance belonging to the division region based on the probability assigned to the division region; and an instance selecting unit that selects the predetermined instance based on the sampling probability on the predetermined instance.

Abstract: An information processing apparatus includes: a calculation unit that adds an index value indicating a degree of uncertainty of a prediction, to each of a plurality of instances, on the basis of the prediction for each of the plurality of instances respectively outputted from a plurality of learning models; a selection unit that selects at least one instance, of which the added index value is included in a predetermined selection range, from the plurality of instances; and an output unit that outputs the selected at least one instance.

Abstract: In an information processing device, an input means accepts training examples formed by features. A label means assigns labels to the training examples. An error calculation means generates one or more student models using the training examples to which the labels are assigned, and calculates errors between predictions of the one or more student models and the labels. An error prediction model generation means generates an error prediction model which is a model for predicting the errors. An output means outputs each example for which the error is predicted to be significant based on the error prediction model.

Abstract: An information processing device, an input means receives training examples formed by features. A label generation means assigns labels to the training examples using a teacher model. An error calculation means generates one or more student models using at least a part of the training examples to which the labels are assigned, and calculates errors between predictions of the one or more student models and predictions of the teacher model by using the error calculation examples different from examples used to generate the one or more student models. A data retention means retains examples formed by features. A data extraction means extracts and outputs each example for which the error is to be significant based on the errors calculated by the error calculation means, from the data retention means.

Abstract: In a rule generation apparatus, a rule generation unit generates a rule group for dividing a training example into a plurality of clusters related to target values using a rule base model so that a “first constraint” is satisfied. The training example includes at least one real example and at least one synthetic example. Each of the real and the synthetic examples includes a feature value vector of which vector elements are one or a plurality of feature values corresponding to feature parameters different from each other, and a target value. The feature value and the target value included in each of the real examples are measured values, while each of the synthetic examples is an example formed based on the real example. The “first constraint” includes a constraint that each of the clusters includes at least N (N is a natural number) real example.