Neural Computing: An Introduction Book

INTRODUCTION Humans and computers The structure of the brain Learning in machines The differences Summary

PATTERN RECOGNITION Introduction Pattern recognition in perspective Pattern recognition-a definition Feature vectors and feature space Discriminant functions Classification techniques Linear classifiers Statistical techniques Pattern recognition-a summary

THE BASIC NEURON Introduction Modeling the single neuron Learning in simple neurons The perceptron: a vectorial perspective The perceptron learning rule: proof Limitations of perceptrons The end of the line?
Summary

THE MULTILAYER PERCEPTRON Introduction Altering the perceptron model The new model The new learning rule The multilayer perceptron algorithm The XOR problem revisited Visualizing network behavior Multilayer perceptrons as classifiers Generalization Fault tolerance Learning difficulties Radial basis functions Applications Summary

KOHONEN SELF-ORGANIZING NETWORKS Introduction The Kohonen algorithm Weight training Neighborhoods Reducing the neighborhood Learning vector quantization (LVQ)
The phonetic typewriter Summary

HOPFIELD NETWORKS Introduction The Hopfield model The energy landscape The Boltzmann machine Constraint satisfaction Summary

ADAPTIVE RESONANCE THEORY Introduction Adaptive resonance theory (ART)
Architecture and operation ART algorithm Training the ART network Classification Conclusion Summary of ART

ASSOCIATIVE MEMORY Standard computer memory Implementing associative memory Implementation in RAMs RAMs and N-tupling Willshaw's associative net The ADAM system Kanerva's sparse distributed memory Bidirectional associative memories Conclusion Summary

INTO THE LOOKING GLASS Overview Hardware and software implementations Optical computing Optical computing and neural networks

INDEX