Items related to Neural Network Learning: Theoretical Foundations
Synopsis
This important work describes recent theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised learning problems, and addresses the key statistical and computational questions. Chapters survey research on pattern classification with binary-output networks, including a discussion of the relevance of the Vapnik Chervonenkis dimension, and of estimates of the dimension for several neural network models. In addition, Anthony and Bartlett develop a model...
Book Description
This book describes recent theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised learning problems, and addresses the key statistical and computational questions. The authors also discuss the computational complexity of neural network learning, describing a variety of hardness results, and outlining two efficient constructive learning algorithms. The book is essentially self-contained, since it introduces the necessary background material on probability, statistics,...
Review
"This book gives a thorough but nevertheless self-contained treatment of neural network learning from the perspective of computational learning theory." Mathematical Reviews
"This book is a rigorous treatise on neural networks that is written for advanced graduate students in computer science. Each chapter has a bibliographical section with helpful suggestions for further reading...this book would be best utilized within an advanced seminar context where the student would be assisted with...
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- PublisherCambridge University Press
- Publication date1999
- ISBN 10 052157353X
- ISBN 13 9780521573535
- BindingHardcover
- LanguageEnglish
- Edition number1
- Number of pages404
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Neural Network Learning: Theoretical Foundations
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Cambridge University Press, 1999
ISBN 10: 052157353X
ISBN 13: 9780521573535
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Neural Network Learning
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Neural Network Learning : Theoretical Foundations
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Neural Network Learning : Theoretical Foundations
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Neural Network Learning: Theoretical Foundations
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Neural Network Learning: Theoretical Foundations
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Neural Network Learning (Hardcover)
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Hardcover. Condition: new. Hardcover. This important work describes recent theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised learning problems, and addresses the key statistical and computational questions. Chapters survey research on pattern classification with binary-output networks, including a discussion of the relevance of the Vapnik Chervonenkis dimension, and of estimates of the dimension for several neural network models. In addition, Anthony and Bartlett develop a model of classification by real-output networks, and demonstrate the usefulness of classification with a "large margin." The authors explain the role of scale-sensitive versions of the Vapnik Chervonenkis dimension in large margin classification, and in real prediction. Key chapters also discuss the computational complexity of neural network learning, describing a variety of hardness results, and outlining two efficient, constructive learning algorithms. The book is self-contained and accessible to researchers and graduate students in computer science, engineering, and mathematics. This important work describes recent theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised learning problems, and addresses the key statistical and computational questions. Chapters survey research on pattern classification with binary-output networks, including a discussion of the relevance of the Vapnik Chervonenkis dimension, and of estimates of the dimension for several neural network models. In addition, Anthony and Bartlett develop a model of classification by real-output networks, and demonstrate the usefulness of classification with a "large margin." The authors explain the role of scale-sensitive versions of the Vapnik Chervonenkis dimension in large margin classification, and in real prediction. Key chapters also discuss the computational complexity of neural network learning, describing a variety of hardness results, and outlining two efficient, constructive learning algorithms. The book is self-contained and accessible to researchers and graduate students in computer science, engineering, and mathematics. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9780521573535
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Neural Network Learning: Theoretical Foundations
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Hardcover. Condition: Brand New. 1st edition. 389 pages. 8.75x6.00x1.25 inches. In Stock. This item is printed on demand. Seller Inventory # __052157353X
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