Includes bibliographical references (p. [349]-359) and index.
CONTENTS NOTE
Text of Note
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.