Learning and generalization : with applications to neural networks

Bibliographic Information

Learning and generalization : with applications to neural networks

M. Vidyasagar

(Communications and control engineering)

Springer, c2003

2nd ed.

Other Title

Learning and generalisation : with applications to neural networks

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Note

Bibliography: p. [475]-484

Includes index

Their is a miss print on title page: generalisation

Description and Table of Contents

Description

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.

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.

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