Learning and Generalisation: With Applications to Neural Networks / Edition 2

by Mathukumalli Vidyasagar

Overview

How does a machine learn a new concept on the basis of examples? This second edition takes account of important new developments in the field. It also deals extensively with the theory of learning control systems, now comparably mature to learning of neural networks.

Product Details

ISBN-13:	9781849968676
Publisher:	Springer London
Publication date:	12/08/2010
Series:	Communications and Control Engineering
Edition description:	Softcover reprint of hardcover 2nd ed. 2002
Pages:	488
Product dimensions:	6.10(w) x 9.25(h) x 0.04(d)

Table of Contents

1. Introduction.- 2. Preliminaries.- 3. Problem Formulations.- 4. Vapnik-Chervonenkis, Pseudo- and Fat-Shattering Dimensions.- 5. Uniform Convergence of Empirical Means.- 6. Learning Under a Fixed Probability Measure.- 7. Distribution-Free Learning.- 8. Learning Under an Intermediate Family of Probabilities.- 9. Alternate Models of Learning.- 10. Applications to Neural Networks..- 11. Applications to Control Systems.- 12. Some Open Problems.