Bayesian Learning for Neural Networks

Neal Radford M.

ISBN 10: 0387947248 ISBN 13: 9780387947242

Editorial: Springer, 1996

Idioma: Ingl�s

Condici�n: Nuevo Encuadernaci�n de tapa blanda

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pp. 204 49:B&W 6.14 x 9.21 in or 234 x 156 mm (Royal 8vo) Perfect Bound on White w/Gloss Lam.

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Detalles bibliogr�ficos

T�tulo: Bayesian Learning for Neural Networks
Editor: Springer
Fecha de publicaci�n: 1996
Idioma: Ingl�s
ISBN 10: 0387947248
ISBN 13: 9780387947242
Encuadernaci�n: Encuadernaci�n de tapa blanda
Estado: New

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Sinopsis

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.

Acerca del 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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