Learning and generalization : with applications to neural networks
著者
書誌事項
Learning and generalization : with applications to neural networks
（Communications and control engineering）
Springer, c2003
2nd ed.
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Learning and generalisation : with applications to neural networks
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注記
Bibliography: p. [475]-484
Includes index
Their is a miss print on title page: generalisation
内容説明・目次
内容説明
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.
目次
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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