  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.
