How to Formulate and Solve Statistical Recognition and Learning Problems
Abstract
We formulate problems of statistical recognition and learning 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 which embraces the whole range of sizes of the learning sample, including the zero size. Learning becomes a special case of recognition without learning. We define the concept of closest to optimal strategy, being a solution to the formulated problem, and describe a technique for finding such a strategy. On several illustrative cases, the strategy is shown to be superior to the widely used learning methods based on maximal likelihood estimation.
Cite
@article{arxiv.1509.08830,
title = {How to Formulate and Solve Statistical Recognition and Learning Problems},
author = {Michail Schlesinger and Evgeniy Vodolazskiy},
journal= {arXiv preprint arXiv:1509.08830},
year = {2015}
}