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Computational Learning Theory: An Introduction: 30 (Cambridge Tracts in Theoretical Computer Science, Series Number 30) - Softcover
Synopsis
Computational learning theory is a subject which has been advancing rapidly in the last few years. The authors concentrate on the probably approximately correct model of learning, and gradually develop the ideas of efficiency considerations. Finally, applications of the theory to artificial neural networks are considered. Many exercises are included throughout, and the list of references is extensive. This volume is relatively self contained as the necessary background material from logic, probability and complexity theory is included. It will therefore form an introduction to the theory of computational learning, suitable for a broad spectrum of graduate students from theoretical computer science and mathematics.
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Computational Learning Theory This an introduction to the theory of computational learning. Full description
Review
."..clean, readable, self-contained treatment of the foundations of PAC learnability theory." R. Roos, Computing Reviews ."..notable for its clean, readable, self-contained treatment of the foundations of PAC learnability theory." R. Roos, Artificial Intelligence ."..a welcome addition to the limited range of literature on computational learning theory, and it should perform a useful service in alerting a wider audience to this interesting and lively area..." Mathematical reviews
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- PublisherCambridge University Press
- Publication date1997
- ISBN 10 0521599229
- ISBN 13 9780521599221
- BindingPaperback
- LanguageEnglish
- Number of pages172
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Computational Learning Theory (Cambridge Tracts in Theoretical Computer Science, Series Number 30)
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ISBN 13: 9780521599221
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Computational Learning Theory
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Computational Learning Theory
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Computational Learning Theory (Cambridge Tracts in Theoretical Computer Science)
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Computational Learning Theory (Cambridge Tracts in Theoretical Computer Science, Series Number 30)
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Computational Learning Theory: An Introduction
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Computational Learning Theory
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Computational Learning Theory (Paperback)
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ISBN 10: 0521599229
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Paperback. Condition: new. Paperback. Computational learning theory is a subject which has been advancing rapidly in the last few years. The authors concentrate on the probably approximately correct model of learning, and gradually develop the ideas of efficiency considerations. Finally, applications of the theory to artificial neural networks are considered. Many exercises are included throughout, and the list of references is extensive. This volume is relatively self contained as the necessary background material from logic, probability and complexity theory is included. It will therefore form an introduction to the theory of computational learning, suitable for a broad spectrum of graduate students from theoretical computer science and mathematics. Computational learning theory is one of the first attempts to construct a mathematical theory of a cognitive process. It has been a field of much interest and rapid growth in recent years. This text provides a framework for studying a variety of algorithmic processes, such as those currently in use for training artificial neural networks. The authors concentrate on an approximate model for learning and gradually develop the ideas of efficiency considerations. Finally, they consider applications of the theory to artificial neural networks. An abundance of exercises and an extensive list of references round out the text. This volume provides a comprehensive review of the topic, including information drawn from logic, probability, and complexity theory. It forms a solid introduction to the theory of comptutational learning suitable for a broad spectrum of graduate students from theoretical computer science to mathematics. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9780521599221
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Computational Learning Theory (Cambridge Tracts in Theoretical Computer Science, Series Number 30)
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ISBN 10: 0521599229
ISBN 13: 9780521599221
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