Includes bibliographical references (pages 847-887) and index.

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: Kernal Merhods 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 889.

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.

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.