Author: Simon Haykin
Publishers: Prentice Hall International, Inc.
About this Book:
Neural Networks or artificial neural networks to be more precise, represent a technology that is rooted in many disciplines: neuroscience, mathematics, statistics, physics, computer science and engineering.
This book provides a comprehensive foundation of neural networks, recognizing the multidisciplinary nature of the subject. The material presented in this book is supported with examples. computer-oriented experiments, end-of-chapter problems and a bibliography.
- Learning Processes
- Single Layer Perceptrons
- Multilayer Perceptrons
- Radial-Basis Function Networks
- Support Vector Machines
- Committee Machines
- Principal Components Analysis
- Self-Organizing Maps
- Information-Theoretic Models
- Stochastic Machines And Their Approximates Rooted In Statistical Mechanics
- Neurodynamic Programming
- Temporal Processing Using Feedforward Networks
- Dynamically Driven Recurrent Networks
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