An Introduction to Computational Learning Theory

— —

Michael J. Kearns (Computer/Info Sci- 509 Levine) Umesh Vazirani (Computer Science Division)

An Introduction to Computational Learning Theory

Michael J. Kearns (Computer/Info Sci- 509 Levine), Umesh Vazirani (Computer Science Division)

$170

Hardback

Not in-store but you can order this
How long will it take?

Availability Information

We source books from suppliers in Australia and overseas. For books we don't currently have in stock, the time it takes to get them from our suppliers can vary widely - from a few days to a few months - so we check each book with each supplier to determine the expected time it will take to be supplied to us.

We then advise you accordingly. If the time taken to get any book is too long for you, you can let us know and we will cancel or adjust your order, and refund as required.

To find out the anticipated arrival time for specific items prior to ordering, please contact us by phone or email:

Phone +61 2 9264 3111, or 1800 4 BOOKS (1800 4 26657) if outside Sydney:
option 1 Abbey's Bookshop (Crime, History, Science, Kids & more) • info@abbeys.com.au
option 2 Language Book Centre (ESL & Foreign Languages) • language@abbeys.com.au
option 3 Galaxy Bookshop (Sci-fi, Fantasy, Romance, Graphic Novels) • sf@galaxybooks.com.au

QTY:

English

MIT Press
15 August 1994

Machine learning; Neural networks & fuzzy systems

Series: The MIT Press

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.

By: Michael J. Kearns (Computer/Info Sci- 509 Levine), Umesh Vazirani (Computer Science Division)
Imprint: MIT Press
Country of Publication: United States
Dimensions: Height: 229mm, Width: 178mm, Spine: 17mm
Weight: 590g
ISBN: 9780262111935
ISBN 10: 0262111934
Series: The MIT Press
Pages: 222
Publication Date: 15 August 1994
Recommended Age: From 18 years
Audience: Professional and scholarly , Professional and scholarly , Professional & Vocational , Undergraduate , Further / Higher Education
Format: Hardback
Publisher's Status: Active

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.