LEARNING IN PATTERN RECOGNITION

A discussion is made of some nonparametric approaches to the problem of the classification of an unknown pattern when the only information on the underlying distributions associated with the various categories is that which can be obtained from a finite number of samples.

Abstract

This paper is specifically concerned with the problem of inferring from a finite set of patterns the classification of an unknown pattern. A discussion of the general problems inherent in the concept of "learning" and "data reduction" are discussed from a standpoint of measurement selection for the general pattern recognition problem. A brief history of the existent work in empirical Bayes and compound sequential Bayes procedures will be presented. It is felt that these procedures are basically non-Bayesian, despite their names, and are therefore especially suited to problems arising in pattern recognition. Finally, a discussion is made of some nonparametric approaches to the problem of the classification of an unknown pattern when the only information on the underlying distributions associated with the various categories is that which can be obtained from a finite number of samples.