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An Introduction to Computational Learning Theory


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Table of Contents

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.

About the Author

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.

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