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Mar 1993
ISBN 0262071452
364 pp.
156 illus.
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Neural Network Learning and Expert Systems
Stephen I. Gallant

"A gold mine for researchers working on learning algorithms and computer professionals who want to use them."
-- Mario Marchand, Physics Department, University of Ottawa

Neural Network Learning and Expert Systems is the first book to present a unified and in-depth development of neural network learning algorithms and neural network expert systems. Especially suitable for students and researchers in computer science, engineering, and psychology, this text and reference provides a systematic development of neural network learning algorithms from a computational perspective, coupled with an extensive exploration of neural network expert systems which shows how the power of neural network learning can be harnessed to generate expert systems automatically.

Features include a comprehensive treatment of the standard learning algorithms (with many proofs), along with much original research on algorithms and expert systems. Additional chapters explore constructive algorithms, introduce computational learning theory, and focus on expert system applications to noisy and redundant problems.

For students there is a large collection of exercises, as well as a series of programming projects that lead to an extensive neural network software package. All of the neural network models examined can be implemented using standard programming languages on a microcomputer.

Stephen l. Gallant taught courses in neural network learning and expert systems as Associate Professor of Computer Science at Northeastern University. He is currently a Senior Scientist at HNC, Inc.

Table of Contents
 FOREWORD
1 Introduction and Important Definitions
2 Representation Issues
3 Perceptron Learning and the Pocket Algorithm
4 Winner-Take-All Groups or Linear Machines
5 Autoassociators and One-Shot Learning
6 Mean Squared Error (MSE) Algorithms
7 Unsupervised Learning
8 The Distributed Method and Radial Basis Functions
9 Computational Learning Theory and the BRD Algorithm
10 Constructive Algorithms
11 Backpropagation
12 Backpropagation: Variations and Applications
13 Simulated Annealing and Boltzmann Machines
14 Expert Systems and Neural Networks
15 Details of the MACIE System
16 Noise, Redundancy, Fault Detection, and Bayesian Decision Theory
17 Extracting Rules from networks
 Appendix Representation Comparisons
 Bibliography
 INDEX
 
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