A survey of some aspects of computational learning theory

Abstract

In the introduction of his paper starting computational learning theory, Valiant observed that the intuitive notion of learning merits similar attention from the point of view of formal theoretical study as that of the notion of computing. In this comparison, learning appears to be more elusive, more difficult to capture by a unified mathematical theory (as noted by Haussler (1990), it is not clear whether such a theory is even possible or desirable). Research was focused on concept learning, which is in fact closely related to computing in that several approaches developed in theoretical computer science can be adapted to its study. Interesting connections were found with other fields such as combinatorial optimization, cryptography and statistical pattern recognition. In this survey we gave a short account of some aspects of the results obtained in computational learning theory, by describing several learning models, characterizations of learnability, some learning algorithms and negative results.