Neural Networks, A Comprehensive Foundation, 2E

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.



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