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Computational learning theory applied to discrete-time cellular neural networks

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2 Author(s)
Utschick, W. ; Inst. for Network Theory & Circuit Design, Tech. Univ. Munchen ; Nossek, J.A.

The theory of probably approximately correct (PAC) learning is applied to discrete-time cellular neural networks (DTCNNS). The Vapnik-Chervonenkis dimension of DTCNN is determined. Considering two different operation modes of the network, an upper bound of the sample size for a reliable generalization of DTCNN architecture is given

Published in:

Cellular Neural Networks and their Applications, 1994. CNNA-94., Proceedings of the Third IEEE International Workshop on

Date of Conference:

18-21 Dec 1994