Minimax Deviation Learning with Short Learning Samples - Prof. Michail Schlesinger
Yandex School of Data Analysis Conference
Machine Learning: Prospects and Applications
https://yandexdataschool.com/conference
We formulate problems of learning and recognition in a common framework of complex hypothesis testing. Based on arguments from multi-criteria optimization, we identify strategies that are improper for solving these problems and derive a common form of the remaining strategies. We show that some widely used approaches to recognition and learning are improper in this sense. We then propose a generalized formulation of the recognition and learning problem that embraces the whole range of sizes of the learning sample, including zero size. Learning becomes a special case of recognition. We define the concept of minimax deviation Bayesian learning, being a solution to the formulated problem. In several illustrative cases, the strategy is shown to be superior to the widely used learning methods based on maximal likelihood estimation.