- 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.
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.
(source: Nielsen Book Data)
