Bayesian Learning for Neural Networks

Radford M. Neal

ISBN 10: 0387947248 / ISBN 13: 9780387947242

Published by Springer New York Aug 1996, 1996

About this Item

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.

Seller Inventory # 9780387947242

Bibliographic Details

• Title: Bayesian Learning for Neural Networks
• Publisher: Springer New York Aug 1996
• Publication Date: 1996
• Language: English
• ISBN 10: 0387947248
• ISBN 13: 9780387947242
• Binding: Taschenbuch
• Condition: Neu

About this title

Synopsis

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.