Neural Networks and Learning Machines

✍ Scribed by Simon O. Haykin

• Publisher: Prentice Hall
• Year: 2008
• Tongue: English
• Leaves: 937
• Edition: 3rd Edition
• Category: Library

✦ Synopsis

For graduate-level neural network courses offered in the departments of Computer Engineering, Electrical Engineering, and Computer Science.

 

Neural Networks and Learning Machines, Third Edition is renowned for its thoroughness and readability. This well-organized and completely up-to-date text remains the most comprehensive treatment of neural networks from an engineering perspective. This is ideal for professional engineers and research scientists.

 

Matlab codes used for the computer experiments in the text are available for download at: http://www.pearsonhighered.com/haykin/

 

Refocused, revised and renamed to reflect the duality of neural networks and learning machines, this edition recognizes that the subject matter is richer when these topics are studied together. Ideas drawn from neural networks and machine learning are hybridized to perform improved learning tasks beyond the capability of either independently.

✦ Table of Contents

Preface
Introduction
Chapter 1. Rosenblatt’s Perceptron
Chapter 2. Model Building through Regression
Chapter 3. The Least-Mean-Square Algorithm
Chapter 4. Multilayer Perceptrons
Chapter 5. Kernel Methods and Radial-Basis Function Networks
Chapter 6. Support Vector Machines
Chapter 7. Regularization Theory
Chapter 8. Principal-Components Analysis
Chapter 9. Self-Organizing Maps
Chapter 10. Information-Theoretic Learning Models
Chapter 11. Stochastic Methods Rooted in Statistical Mechanics
Chapter 12. Dynamic Programming
Chapter 13. Neurodynamics
Chapter 14. Bayseian Filtering for State Estimation of Dynamic Systems
Chapter 15. Dynamically Driven Recurrent Networks
Bibliography
Index