Haykin S. Neural Networks and Learning Machines

Third Edition. — Pearson Education, 2009, — 937 p.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.Rosenblatt’s Perceptron
Model Building through Regression
The Least-Mean-Square Algorithm
Multilayer Perceptrons
Kernel Methods and Radial-Basis Function Networks
Support Vector Machines
Regularization Theory
Principal-Components Analysis
Self-Organizing Maps
Information-Theoretic Learning Models
Stochastic Methods Rooted in Statistical Mechanics
Dynamic Programming
Neurodynamics
Bayseian Filtering for State Estimation of Dynamic Systems
Dynamically Driven Recurrent Networks