Description: Bayesian Learning for Neural Networks, Paperback by Neal, Radford M., ISBN 0387947248, ISBN-13 9780387947242, Brand New, Free shipping in the US 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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Book Title: Bayesian Learning for Neural Networks
Item Length: 9.3 in
Item Width: 6.1 in
Author: Radford M. Neal
Publication Name: Bayesian Learning for Neural Networks
Format: Trade Paperback
Language: English
Subject: Machine Theory, Probability & Statistics / General, Computer Simulation, Neural Networks, Intelligence (Ai) & Semantics, Probability & Statistics / Bayesian Analysis
Publisher: Springer New York
Series: Lecture Notes in Statistics Ser.
Publication Year: 1996
Type: Textbook
Subject Area: Computers, Mathematics
Item Weight: 22.9 Oz
Number of Pages: 204 Pages
