Titlebook: Learning and Generalisation; With Applications to M. Vidyasagar Book 2003Latest edition Springer-Verlag London 2003 Computer.Control Theory

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Book 2003Latest edition on the basis of examples?..• How can a neural network, after training, correctly predict the outcome of a previously unseen input?..• How much training is required to achieve a given level of accuracy in the prediction?..• How can one identify the dynamical behaviour of a nonlinear control system b

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Some Open Problems, are retained, and are discussed in that order. Wherever the problem has been solved, the solution is briefly summarized. In case the problem still remains open, the original motivating discussion is retained.

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Introduction,form not merely numerical computations, but also more human-like tasks such as learning new concepts, solving problems, and so on. One of the objectives of the present monograph is to formulate one specific class of models of “learning” that seems natural for a machine, and to explore which types of

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Vapnik-Chervonenkis, Pseudo- and Fat-Shattering Dimensions,is rather unfortunate, as the three “dimensions” have nothing at all to do with the dimension of a vector space, except in very special situations. Rather, these “dimensions” are combinatorial parameters that measure the “richness” of concept classes or function classes. The Vapnik-Chervonenkis dime

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