Learning device, learning method, and computer-readable medium

A learning method by a learning device configured to performs supervised learning of a class classification problem, the learning method comprising:

• inputting target data to be learned, class label information of the target data, and statistical property information of the target data;
• extracting, by a feature amount extractor, a feature amount from the target data by using a parameter;
• outputting, by a class classifier, a class classification inference result of the target data by statistical processing using the feature amount and a weight vector of each class;
• calculating a loss by using a loss function in which the class classification inference result and the class label information are taken as inputs;
• and
• correcting the weight vector of the class classifier and the parameter of the feature amount extractor in such a way that the loss is reduced, according to the statistical property information.

A non-transitory computer-readable medium storing a program causing a computer that performs supervised learning of a class classification problem to execute:

• processing of inputting target data to be learned, class label information of the target data, and statistical property information of the target data;
• processing of extracting, by a feature amount extractor, a feature amount from the target data by using a parameter;
• processing of outputting, by a class classifier, a class classification inference result of the target data by statistical processing using the feature amount and a weight vector of each class;
• processing of calculating a loss by using a loss function in which the class classification inference result and the class label information are taken as inputs;
• and
• processing of correcting the weight vector of the class classifier and the parameter of the feature amount extractor in such a way that the loss is reduced, according to the statistical property information.