An Introduction to Computational Learning Theory
Kearns, Michael J.; Vazirani, Umesh V.
9780262111935
ISBN 13: 9780262111935
Publication Date: 1994
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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.
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An Introduction to Computational Learning Theory (ISBN: 9780262111935) Kearns, Michael J.; Vazirani, Umesh V. Quantity Available: 2
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An Introduction to Computational Learning Theory (Hardback) (ISBN: 9780262111935) Kearns, Michael J.; Vazirani, Umesh V. Quantity Available: 1
Book Description: MIT Press Ltd, United States, 1994. Hardback. Book Condition: New. 235 x 180 mm. Brand New Book with Free Worldwide Delivery. 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. Bookseller Inventory # AAH9780262111935 Bookseller & Payment Information | More Books from this Seller | Ask Bookseller a Question |
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An Introduction to Computational Learning Theory (ISBN: 9780262111935) Kearns, Michael J.; Vazirani, Umesh V. Quantity Available: 2
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An Introduction to Computational Learning Theory (ISBN: 9780262111935) Kearns, Michael J.; Vazirani, Umesh V. Quantity Available: 1
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An Introduction to Computational Learning Theory (ISBN: 0262111934 / 0-262-11193-4) Kearns, Michael J.; Vazirani, Umesh V. Quantity Available: > 20
Book Description: Mit Pr, 1994. Hardcover. Book Condition: Brand New. 221 pages. 9.50x7.25x0.75 inches. In Stock. Bookseller Inventory # __0262111934 Bookseller & Payment Information | More Books from this Seller | Ask Bookseller a Question |
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