Learning and generalization : with applications to neural networks
Author(s)
Bibliographic Information
Learning and generalization : with applications to neural networks
(Communications and control engineering)
Springer, c2003
2nd ed.
- Other Title
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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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