Abstract: Transfer learning for a target domain that partially matches classification of a source domain having a ground truth is enabled. A learning device stores a first set subjected to classification and assigned with a ground truth and a second set having a class that partially matches the first set, and executes, based on a first loss function of first data and a second loss function of second data, processing of updating, by the number of updates, an identifier for identifying the first data and the second data when a feature of the first data or a feature of the second data is input. The learning device calculates a similarity at which the first data is similar to the second data by a data selector that calculates the similarity at the last time, and updates the data selector and the first distribution based on the estimated value.

Abstract: A machine learning system determines whether an influence which exclusion and addition of evaluation target data from and to learning data has on the performance of a machine learning model includes: an acquisition unit that acquires an initial data group used to learn a learning model, evaluation target data added to, or excluded from, the initial data group, and a verification data group including at least one element which is not included in the evaluation target data; and a contribution degree calculation unit that calculates a contribution degree for evaluating an influence which the evaluation target data has on performance of the learning model, on the basis of an output value by the learning model for which the verification data group is input, and an output value by a relearning model which is learned by adding or excluding the evaluation target data to or from the initial data group.

Abstract: A computer, which is configured to generate learning data for use in machine learning for generating model information to be set to a system that is configured to generate second output data from first output data, the first output data being generated by processing input data with use of the model information, the computer being configured to: obtain analysis input data; generate, from the analysis input data, first to-be-analyzed output data based on an arbitrary generation condition; generate second to-be-analyzed output data from the first to-be-analyzed output data; analyze the second to-be-analyzed output data; and generate, as the learning data, data including the analysis input data and the first to-be-analyzed output data in a case where the second to-be-analyzed output data fulfills a user's demand.