CONTENIDO:
Introduction
What is a Neural Network?
The Human Brain
Models of a Neuron
Neural Networks Viewed As Directed Graphs
Feedback
Network Architectures
Knowledge Representation
Learning Processes
Learning Tasks
Concluding Remarks
1. Rosenblatt¿s Perceptron
2. Model Building through Regression
3. The Least-Mean-Square Algorithm
4. Multilayer Perceptrons
5. Kernel Methods and Radial-Basis Function Networks
6. Support Vector Machines
7. Regularization Theory
8. Principal-Components Analysis
9. Self-Organizing Maps
10. Information-Theoretic Learning Models
11. Stochastic Methods Rooted in Statistical Mechanics
12. Dynamic Programming
13. Neurodynamics
14. Bayseian Filtering for State Estimation of Dynamic Systems
15. Dynamically Driven Recurrent Networks
Notes and References
Problems
Bibliography
Index
