Articoli correlati a Bayesian Learning for Neural Networks: 118
Sinossi
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.
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Informazioni sull?autore
Radford M. Neal is an Assistant Professor in the Departments of Statistics and Computer Science at the University of Toronto.
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Bayesian Learning for Neural Networks (Lecture Notes in Statistics, 118)
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Softcover. Condizione: Très bon. Ancien livre de bibliothèque avec équipements. Edition 1996. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Very good. Former library book. Edition 1996. Ammareal gives back up to 15% of this item's net price to charity organizations. Codice articolo G-162-093
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Bayesian Learning for Neural Networks (Lecture Notes in Statistics, 118, Band 118)
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Bayesian Learning for Neural Networks (Lecture Notes in Statistics, 118)
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Springer (edition 1996), 1996
ISBN 10: 0387947248
ISBN 13: 9780387947242
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Bayesian Learning for Neural Networks (Lecture Notes in Statistics, 118)
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Bayesian Learning for Neural Networks (Lecture Notes in Statistics, 118)
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 Bayesi. Codice articolo 5912158
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Bayesian Learning for Neural Networks (Lecture Notes in Statistics, 118)
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Bayesian Learning for Neural Networks (Lecture Notes in Statistics, 118)
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Bayesian Learning for Neural Networks
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - 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. 204 pp. Englisch. Codice articolo 9780387947242
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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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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
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Taschenbuch. Condizione: 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. Codice articolo 9780387947242
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