Emphasizing issues of computational efficiency, Michael Kearns and Umesh                  Vazirani introduce a number of central topics in computational learning theory for                  researchers and students in artificial intelligence, neural networks, theoretical                  computer science, and statistics.Computational learning theory is a new and rapidly                  expanding area of research that examines formal models of induction with the goals                  of discovering the common methods underlying efficient learning algorithms and                  identifying the computational impediments to learning.Each topic in the book has                  been chosen to elucidate a general principle, which is explored in a precise formal                  setting. Intuition has been emphasized in the presentation to make the material                  accessible to the nontheoretician while still providing precise arguments for the                  specialist. This balance is the result of new proofs of established theorems, and                  new presentations of the standard proofs.The topics covered include the motivation,                  definitions, and fundamental results, both positive and negative, for the widely                  studied L. G. Valiant model of Probably Approximately Correct Learning; Occam's                  Razor, which formalizes a relationship between learning and data compression; the                  Vapnik-Chervonenkis dimension; the equivalence of weak and strong learning;                  efficient learning in the presence of noise by the method of statistical queries;                  relationships between learning and cryptography, and the resulting computational                  limitations on efficient learning; reducibility between learning problems; and                  algorithms for learning finite automata from active experimentation.

An introduction to computational learning theory PDF

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