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A Theory of Learning and Generalization

A Theory of Learning and Generalization With Applications to Neural Networks and Control Systems - Communications and Control Engineering Series

Hardback (01 Dec 1996) | German

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Publisher's Synopsis

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.

Book information

ISBN: 9783540761204
Publisher: Springer
Imprint: Springer
Pub date:
DEWEY: 006.31
DEWEY edition: 20
Language: German
Number of pages: 383
Weight: -1g
Height: 240mm