Introduction to Computational Learning Theory

Name: Introduction to Computational Learning Theory
Price: 30.98 USD
Availability: InStock
ISBN: 9780262111935

ISBN-10: 0262111934

ISBN-13: 9780262111935

Edition: 1994

Authors: Michael J. Kearns, Umesh Vazirani

List price: $70.00

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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…

Book details

List price: $70.00
Copyright year: 1994
Publisher: MIT Press
Publication date: 8/15/1994
Binding: Hardcover
Pages: 222
Size: 7.13" wide x 9.25" long x 0.75" tall
Weight: 1.540
Language: English

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



Preface



The Probably Approximately Correct Learning Model



Occam's Razor



The Vapnik-Chervonenkis Dimension



Weak and Strong Learning



Learning in the Presence of Noise



Inherent Unpredictability



Reducibility in PAC Learning



Learning Finite Automata by Experimentation



Appendix: Some Tools for Probabilistic Analysis


Bibliography


Index