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
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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.
by "Nielsen BookData"
