Neural Networks: A Comprehensive Foundation, 2/e (精裝)

Description:

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

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. Thoroughly revised.

Table of Contents:

 1. Introduction. 

 2. Learning Processes. 

 3. Single-Layer Perceptrons. 

 4. Multilayer Perceptrons. 

 5. Radial-Basis Function Networks. 

 6. Support Vector Machines. 

 7. Committee Machines. 

 8. Principal Components Analysis. 

 9. Self-Organizing Maps.

10. Information-Theoretic Models.

11. Stochastic Machines & Their Approximates Rooted in Statistical Mechanics.

12. Neurodynamic Programming.

13. Temporal Processing Using Feedforward Networks.

14. Neurodynamics.

15. Dynamically Driven Recurrent Networks.

Epilogue.

Bibliography.

Index.