Neural Networks & Learning Machines 3/e [Haykin] 9780131293762

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.

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.

頁數：936
版次：第3版
年份：2009年
規格：平裝/雙色
ISBN：9780131293762

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