A theory of learning and generalization : with applications to neural networks and control systems
Author(s)
Bibliographic Information
A theory of learning and generalization : with applications to neural networks and control systems
(Communications and control engineering)
Springer, c1997
Available at 37 libraries
  Aomori
  Iwate
  Miyagi
  Akita
  Yamagata
  Fukushima
  Ibaraki
  Tochigi
  Gunma
  Saitama
  Chiba
  Tokyo
  Kanagawa
  Niigata
  Toyama
  Ishikawa
  Fukui
  Yamanashi
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  Kyoto
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  Hyogo
  Nara
  Wakayama
  Tottori
  Shimane
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  Hiroshima
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  Tokushima
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  Ehime
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  Nagasaki
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Note
Bibliography: p. [373]-379
Includes index
Description and Table of Contents
Description
Provides a formal mathematical theory for addressing intuitive questions of the type: How does a machine learn a new concept on the basis of examples? How can a neural network, after sufficient training, correctly predict the output of a previously unseen input? How much training is required to achieve a specified level of accuracy in the prediction? How can one "identify" the dynamical behaviour of a nonlinear control system by observing its input-output behaviour over a finite interval of time? This text treats the problem of machine learning in conjunction with the theory of empirical processes, the latter being a well-established branch of probability theory. The treatment of both topics side by side leads to new insights, as well as new results in both topics.
Table of Contents
Contents: Preface.- Introduction.- Preliminaries.- Problem Formulations.- Vapnik-Chervonenkis and Pollard (Pseudo-) Dimensions.- Uniform Convergence of Empirical Means.- Learning Under a Fixed Probability Measure.- Distribution-ree Learning.- Learning Under an Intermediate Family of Probabilities.- Alternate Models of Learning.- Applications to Neural Networks.- Applications to Control Systems.- Some Open Problems.
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