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Machine learning : a theoretical approach /
This is the first comprehensive introduction to computational learning theory. The author's uniform presentation of fundamental results and their applications offers AI researchers a theoretical perspective on the problems they study. The book presents tools for the analysis of probabilistic mo...
| Cote: | Libro Electrónico |
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| Auteur principal: | |
| Format: | Électronique eBook |
| Langue: | Inglés |
| Publié: |
San Mateo, CA :
M. Kaufmann,
�1991.
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| Sujets: | |
| Accès en ligne: | Texto completo |
| Résumé: | This is the first comprehensive introduction to computational learning theory. The author's uniform presentation of fundamental results and their applications offers AI researchers a theoretical perspective on the problems they study. The book presents tools for the analysis of probabilistic models of learning, tools that crisply classify what is and is not efficiently learnable. After a general introduction to Valiant's PAC paradigm and the important notion of the Vapnik-Chervonenkis dimension, the author explores specific topics such as finite automata and neural networks. The presentation is intended for a broad audience--the author's ability to motivate and pace discussions for beginners has been praised by reviewers. Each chapter contains numerous examples and exercises, as well as a useful summary of important results. An excellent introduction to the area, suitable either for a first course, or as a component in general machine learning and advanced AI courses. Also an important reference for AI researchers. |
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| Description matérielle: | 1 online resource (x, 217 pages) : illustrations |
| Bibliographie: | Includes bibliographical references (pages 207-214) and index. |
| ISBN: | 9780080510538 0080510531 9781493305858 1493305859 1322465592 9781322465593 |