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D�tails bibliographiques

Titre: A Theory of Learning and Generalization : With Applications to Neural Networks and Control Systems (Communications and Control Engineering)
�diteur: Springer Verlag
Date de parution: 1997
Langue: anglais
ISBN � 10 chiffres: 3540761209
ISBN � 13 chiffres: 9783540761204
Reliure: Couverture rigide
�tat de l'article: Good

A propos de ce titre

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.

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