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An introduction to computational learning theory [electronic resource] / Michael J. Kearns, Umesh V. Vazirani.

By: Kearns, Michael J.
Contributor(s): Vazirani, Umesh Virkumar.
Material type: TextTextPublisher: Cambridge, Mass. : MIT Press, c1994Description: 1 online resource (xii, 207 p.) : ill.ISBN: 0585350531 (electronic bk.); 9780585350530 (electronic bk.); 0262276860 (electronic bk.); 9780262276863 (electronic bk.).Subject(s): Machine learning | Artificial intelligence | Algorithms | Neural networks (Computer science) | COMPUTERS -- Enterprise Applications -- Business Intelligence Tools | COMPUTERS -- Intelligence (AI) & Semantics | Apprentissage automatique | Intelligence artificielle | Algorithmes | R�eseaux neuronaux (Informatique) | Machine-learning | Neurale netwerkenGenre/Form: Electronic books.Additional physical formats: Print version:: Introduction to computational learning theory.DDC classification: 006.3 Other classification: 54.72 Online resources: EBSCOhost
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.
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Includes bibliographical references (p. [193]-203) and index.

Description based on print version record.

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.

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Other editions of this work

Introduction to computational learning theory by Kearns Michael J
An introduction to computational learning theory by Kearns, Michael J. ©1994
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