Search the Book Contents
Enter words or phrases that might occur in the text. Tips
| Search all books |
| Search only this Book

Look Up Books You Know
Enter title words, author's name, publisher or ISBN. Tips

Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks
by Russell D. Reed and Robert J. Marks II | ISBN: 0262181908
The MIT Press © 1999, 339 pages |
A practical approach to the application of multilayer perceptron neural networks to real-world problems.

Table of Contents | Colleague Comments
Back Cover |

Synopsis by Pete Loshin How do you make a computer capable of making judgments based on sensory inputs? Instead of building a traditional computer, try emulating the brain. If you're comfortable with college level calculus and statistics as well as knowledgeable about neural networks and perceptrons, Neural Smithing can help. But don't expect any hand-holding: Reed and Marks concentrate on practical hints while staying clear of theoretical explanations. Despite a lack of exercises, this book reads like a graduate level textbook. The first chapters provide a bird's eye view of neural networks and supervised learning, but subsequent chapters quickly zero in on topics in supervised learning, in which systems are "adjusted" to accelerate the learning process. Later chapters discuss specific algorithms and methods, and many include "discussion" or "remarks" sections, containing comments and explanations about what approaches may be more successful than others.

Table of Contents
| Neural Smithing - Supervised Learning in Feedforward Artificial Neural Networks
| Preface
| Chapter 1 | - | Introduction
| Chapter 2 | - | Supervised Learning
| Chapter 3 | - | Single-Layer Networks
| Chapter 4 | - | MLP Representational Capabilities
| Chapter 5 | - | Back-Propagation
| Chapter 6 | - | Learning Rate and Momentum
| Chapter 7 | - | Weight-Initialization Techniques
| Chapter 8 | - | The Error Surface
| Chapter 9 | - | Faster Variations of Back-Propagation
| Chapter 10 | - | Classical Optimization Techniques
| Chapter 11 | - | Genetic Algorithms and Neural Networks
| Chapter 12 | - | Constructive Methods
| Chapter 13 | - | Pruning Algorithms
| Chapter 14 | - | Factors Influencing Generalization
| Chapter 15 | - | Generalization Prediction and Assessment
| Chapter 16 | - | Heuristics for Improving Generalization
| Chapter 17 | - | Effects of Training with Noisy Inputs
| Appendix A | - | Linear Regression
| Appendix B | - | Principal Components Analysis
| Appendix C | - | Jitter Calculations
| Appendix D | - | Sigmoid-like Nonlinear Functions
| Index
| References
| List of Figures
| List of Tables

Back Cover
Artificial neural networks are nonlinear mapping systems whose structure is loosely based on principles observed in the nervous systems of humans and animals. The basic idea is that massive systems of simple units linked together in appropriate ways can generate many complex and interesting behaviors. This book focuses on the subset of feedforward artificial neural networks called multilayer perceptions (MLP). These are the most widely used neural networks, with applications as diverse as finance (forecasting), manufacturing (process control), and science (speech and image recognition). This book presents an extensive and practical overview of almost every aspect of MLP methodology, progressing from an initial discussion of what MLPs are and how they might be used to an in-depth examination of technical factors affecting performance. The book can be used as a tool kit by readers interested in applying networks to specific problems, yet it also presents theory and references outlining the last ten years of MLP research. About the Authors Russell D. Reed is Research Assistant Professor of Electrical Engineering, and Robert J. Marks II is Professor of Electrical Engineering, both at the University of Washington.

Books24x7.com, Inc. © 1999-2001 – Feedback