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Titlebook: Computational Learning Theory; Third European Confe Shai Ben-David Conference proceedings 1997 Springer-Verlag Berlin Heidelberg 1997 Algor

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A minimax lower bound for empirical quantizer design, empirically designed vector quantizer is at least . (..) away from the optimal distortion for some distribution on a bounded subset of .., where . is the number of i.i.d. data points that are used to train the empirical quantizer.
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Vapnik-Chervonenkis dimension of recurrent neural networks,o widely used in learning applications, in particular when time is a relevant parameter. This paper provides lower and upper bounds for the VC dimension of such networks. Several types of activation functions are discussed, including threshold, polynomial, piecewise-polynomial and sigmoidal function
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