An Introduction to Computational Learning Theory

Kearns, Michael J.; Vazirani, Umesh

ISBN 10: 0262111934 / ISBN 13: 9780262111935

Edité par The MIT Press, 1994

A propos de cet article

Corners are bent. Stains on outside cover/inside the book. There are tears on paperback. Spine is twisted/bowed. Cover/Case has some rubbing and edgewear. Access codes, CDs, slipcovers and other accessories may not be included.

N° de réf. du vendeur 2Y6RUU004VSW_ns

Détails bibliographiques

• Titre: An Introduction to Computational Learning Theory
• Éditeur: The MIT Press
• Date de parution: 1994
• Langue: anglais
• ISBN à 10 chiffres: 0262111934
• ISBN à 13 chiffres: 9780262111935
• Reliure: Couverture rigide
• État de l'article: Acceptable

A propos de ce titre

Synopsis

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.

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.

À propos de l?auteur

Michael J. Kearns is Professor of Computer and Information Science at the University of Pennsylvania.

Umesh Vazirani is Roger A. Strauch Professor in the Electrical Engineering and Computer Sciences Department at the University of California, Berkeley.