عنوان

Bayesian learning for neural networks /

(Number (ISBN

0387947248 (softcover : alk. paper)

(Number (ISBN

9780387947242 (softcover : alk. paper)

Erroneous ISBN

0387982221

Number

dltt

Title Proper

Bayesian learning for neural networks /

General Material Designation

[Book]

First Statement of Responsibility

Radford M. Neal.

Place of Publication, Distribution, etc.

New York :

Name of Publisher, Distributor, etc.

Springer,

Date of Publication, Distribution, etc.

1996.

Specific Material Designation and Extent of Item

xi, 183 p. :

Other Physical Details

ill. ;

Dimensions

24 cm.

Series Title

Lecture notes in statistics ;

Volume Designation

118

Text of Note

Includes bibliographical references and index.

Text of Note

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.

0

Text of Note

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 network learning methods. Insight into the nature of these complex Bayesian models is provided by a theoretical investigation of the priors over functions that underlie them. Use of these models in practice is made possible using Markov chain Monte Carlo techniques. Both the theoretical and computational aspects of this work are of wider statistical interest, as they contribute to a better understanding of how Bayesian methods can be applied to complex problems.

Text of Note

Presupposing only the basic knowledge of probability and statistics, this book should be of interest to many researchers in statistics, engineering, and artificial intelligence. Software for Unix systems that implements the methods described is freely available over the Internet.

Bayesian statistical decision theory.

Machine learning.

Neural networks (Computer science)

Class number

Book number

Neal, Radford M.

Date of Transaction

20140218105108.0

Electronic name

[Book]

Y