Learning and Generalization - With Applications to Neural Networks

How does it differ from first edition? Includes new material on:

• support vector machines (SVM's),
• fat shattering dimensions
• applications to neural network learning,
• learning with dependent samples generated by beta-mixing process,
• connections between system identification and learning theory
• probabilistic solution of "intractable" problems in robust control and matrix theory using randomised algorithms.

In addition, solutions to some open problems posed in the first edition are included, and new open problems are added.
The author is a respected authority in the field of control and systems theory.
This new edition, with substantial new material, takes account of important new developments in the theory of learning. It also deals extensively with the theory of learning control systems, which has now reached a level of maturity comparable to that of learning of neural networks.
The book is written in a manner that would suit self-study and contains comprehensive references. The chapters are also written to be as autonomous as possible and contain updated open problems to enhance further research and self-study.
Contents

• Introduction
• Preliminaries
• Problem Formulations
• Vapnik-Chervonenkis, Pseudo- and Fat-Shattering Dimensions
• Uniform Convergence of Empirical Means
• Learning Under a Fixed Probability Measure
• Distribution-Free Learning
• Learning Under an Immediate Family of Probabilities
• Alternate Models of Learning
• Applications to Neural Networks
• Applications to Control Systems
• Some Open Problems