An Introduction to Computational Learning Theory | 
 enlarge | Authors: Michael J. Kearns, Umesh V. Vazirani
Publisher: The MIT Press
Category: Book
List Price: $50.00
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Avg. Customer Rating:  2 reviews
Sales Rank: 166876
Media: Hardcover
Number Of Items: 1
Pages: 221
Shipping Weight (lbs): 1.2
Dimensions (in): 9.3 x 7.4 x 0.7
ISBN: 0262111934
Dewey Decimal Number: 006.3
EAN: 9780262111935
ASIN: 0262111934
Publication Date: August 15, 1994
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Product Description
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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| Customer Reviews:
So far so good October 3, 2008
The few chapters I have read of this book seem good. Good examples which is nice.
This is interesting stuff November 18, 2000
16 out of 17 found this review helpful
Kearns is an impressive researcher, precise and succinct. The material on this book follows a tradition of careful proofs of fundamental issues in learning. I wouldn't think this is material of practical use; for that kind of material I'd recommend the new edition of Duda. Rather, Kearns is one of a team of researchers pushing the frontier of proving what is learnable and what is not, why some representations are good for learning and which are not, the dimensionality of the target problem (related to overfitting) working with prinpled definitions of what it is meant to learn borrowed from computational complexity theory.
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