Learning without a teacher

Abstract

This is a review paper discussing and comparing most of the approaches to learning without a teacher which have been suggested to date. The approaches discussed are divided into six classes: guessing a sequence of hypotheses, modifications of this first approach, approximating probability densities by others more easily computed, estimating parameters of a known decision rule, theoretically exact methods which require approximations in implementation, and some miscellaneous approaches. At present, all these approaches are known to be feasible, but few useful methods of comparing them to see which are best have been developed.