Items related to Bayesian Learning for Neural Networks: 118 (Lecture...
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.
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Synopsis
Demonstrates how Bayesian methods allow complex neural network models to be used without the "overfitting" that can occur with traditional training methods. The text presents an insight into complex Baynesian models by a theoretical investigation of priors over functions that underlie them.
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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- PublisherSpringer
- Publication date1996
- ISBN 10 0387947248
- ISBN 13 9780387947242
- BindingPaperback
- Number of pages204
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Bayesian Learning for Neural Networks (Lecture Notes in Statistics, 118)
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