Bayesian Learning for Neural Networks, Radford M. Neal

Description: 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.B2

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Series: N/A

Subject Area: Computer Science

Book Title: Bayesian Learning for Neural Networks (Lecture Notes in Statistic

Educational Level: Adult & Further Education

Level: Advanced

Features: N/A

Country/Region of Manufacture: United Kingdom

Item Height: 235mm

Item Width: 155mm

Author: Radford M. Neal

Publication Name: Bayesian Learning for Neural Networks

Format: Paperback

Language: English

Publisher: Springer-Verlag NY Inc.

Subject: Computer Science, Mathematics

Publication Year: 1996

Type: Textbook

Item Weight: 650g

Number of Pages: 204 Pages