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Bayesian learning for neural networks

Author: Radford M Neal
Publisher: New York : Springer, 1996.
Series: Lecture notes in statistics (Springer-Verlag), v. 118.
Edition/Format:   Print book : EnglishView all editions and formats
Database:WorldCat
Summary:
Artificial "neural networks" are now widely used as flexible models for regression classification applications, but questions remain regarding what these models mean, and how they can safely be used when training data is limited. Bayesian Learning for Neural Networks shows that Bayesian methods allow complex neural network models to be used without fear of the "overfitting" that can occur with traditional neural
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Material Type: Internet resource
Document Type: Book, Internet Resource
All Authors / Contributors: Radford M Neal
ISBN: 0387947248 9780387947242
OCLC Number: 34894370
Description: xi, 183 pages : illustrations ; 24 cm.
Contents: 1. Introduction --
2. Priors for Infinite Networks --
3. Monte Carlo Implementation --
4. Evaluation of Neural Network Models --
5. Conclusions and Further Work --
A. Details of the Implementation --
B. Obtaining the software.
Series Title: Lecture notes in statistics (Springer-Verlag), v. 118.
Responsibility: Radford M. Neal.
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Abstract:

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  Read more...

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