Bayesian Learning for Neural Networks (Lecture Notes in Statistics, 118)
Neal, Radford M.
ISBN 10: 0387947248 ISBN 13: 9780387947242
Published by Springer, 1996
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Artificial "neural networks" are widely used as flexible models for classification and regression applications, but questions remain about how the power of these models can be safely exploited when training data is limited. This book demonstrates how Bayesian methods allow complex neural network models to be used without fear of the "overfitting" that can occur with traditional training methods. Insight into the nature of these complex Bayesian models is provided by a theoretical investigation of the priors over functions that underlie them. A practical implementation of Bayesian neural network learning using Markov chain Monte Carlo methods is also described, and software for it is freely available over the Internet. Presupposing only basic knowledge of probability and statistics, this book should be of interest to researchers in statistics, engineering, and artificial intelligence.
About the Author: Radford M. Neal is an Assistant Professor in the Departments of Statistics and Computer Science at the University of Toronto.
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Title: Bayesian Learning for Neural Networks (...
Publisher: Springer
Publication Date: 1996
Binding: Soft cover
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Taschenbuch. Condition: Neu. Bayesian Learning for Neural Networks | Radford M. Neal | Taschenbuch | 204 S. | Englisch | 1996 | Springer | EAN 9780387947242 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. Seller Inventory # 102121039
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Bayesian Learning for Neural Networks
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Springer New York, Springer US Aug 1996, 1996
ISBN 10: 0387947248
ISBN 13: 9780387947242
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Taschenbuch. Condition: Neu. Neuware -Artificial 'neural networks' are widely used as flexible models for classification and regression applications, but questions remain about how the power of these models can be safely exploited when training data is limited. This book demonstrates how Bayesian methods allow complex neural network models to be used without fear of the 'overfitting' that can occur with traditional training methods. Insight into the nature of these complex Bayesian models is provided by a theoretical investigation of the priors over functions that underlie them. A practical implementation of Bayesian neural network learning using Markov chain Monte Carlo methods is also described, and software for it is freely available over the Internet. Presupposing only basic knowledge of probability and statistics, this book should be of interest to researchers in statistics, engineering, and artificial intelligence.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 204 pp. Englisch. Seller Inventory # 9780387947242