Learning device, learning method, and computer program product

A learning method comprising:

• performing a plurality of selection processes of selecting a plurality of groups including one or more learning samples from a learning sample storage unit that stores a plurality of learning samples in which respective learning samples are classified into any one of a plurality of categories;
• learning a classification metric that classifies the plurality of groups for each of the plurality of selected groups and obtaining a set of a classification metric;
• and
• evaluating that includes
• acquiring two or more evaluation samples of different categories from an evaluation sample storage unit that stores a plurality of evaluation samples in which respective evaluation samples are classified into any one of a plurality of categories,
• evaluating the classification metric included in the set of the classification metric using the two or more acquired evaluation samples,
• acquiring a plurality of classification metric corresponding to the evaluation results from the set of the classification metric, and
• generating an evaluation metric including the plurality of classification metric.

A computer program product comprising a non-transitory computer readable medium including programmed instructions, wherein the instructions, when executed by a computer, cause the computer to perform:

• performing a plurality of selection processes of selecting a plurality of groups including one or more learning samples from a learning sample storage unit that stores a plurality of learning samples in which respective learning samples are classified into any one of a plurality of categories;
• learning a classification metric that classifies the plurality of groups for each of the plurality of selected groups and obtaining a set of a classification metric;
• and
• evaluating that includes
• acquiring two or more evaluation samples of different categories from an evaluation sample storage unit that stores a plurality of evaluation samples in which respective evaluation samples are classified into any one of a plurality of categories,
• evaluating the classification metric included in the set of the classification metric using the two or more acquired evaluation samples,
• acquiring a plurality of classification metric corresponding to the evaluation results from the set of the classification metric, and
• generating an evaluation metric including the plurality of classification metric.