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Inhaltsangabe
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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Über die Autorin bzw. den Autor
Radford M. Neal is an Assistant Professor in the Departments of Statistics and Computer Science at the University of Toronto.
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- VerlagSpringer
- Erscheinungsdatum2013
- ISBN 10 0387947248
- ISBN 13 9780387947242
- EinbandTapa blanda
- SpracheEnglisch
- Anzahl der Seiten204
- Kontakt zum HerstellerNicht verfügbar
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Bayesian Learning for Neural Networks (Lecture Notes in Statistics, 118)
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Zustand: 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. Bestandsnummer des Verkäufers 5912158
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Bayesian Learning for Neural Networks
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Taschenbuch. Zustand: 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. Bestandsnummer des Verkäufers 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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Taschenbuch. Zustand: 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. Bestandsnummer des Verkäufers 9780387947242
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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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Springer New York, Springer US, 1996
ISBN 10: 0387947248
ISBN 13: 9780387947242
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Bestandsnummer des Verkäufers 9780387947242
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Bayesian Learning for Neural Networks (Lecture Notes in Statistics, 118)
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