Bayesian Learning for Neural Networks (Lecture Notes in Statistics, 118, Band 118)

Neal Radford, M.:

ISBN 10: 0387947248 / ISBN 13: 9780387947242

Published by Springer, 2013

About this Item

204 Seiten; 9780387947242.4 Gewicht in Gramm: 500. Seller Inventory # 912848

Bibliographic Details

Title: Bayesian Learning for Neural Networks (...

Publisher: Springer

Publication Date: 2013

Binding: paperback

Condition: Befriedigend

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.