DocumentCode
2516762
Title
Computational learning theory applied to discrete-time cellular neural networks
Author
Utschick, Wolfgang ; Nossek, Josef A.
Author_Institution
Inst. for Network Theory & Circuit Design, Tech. Univ. Munchen
fYear
1994
fDate
18-21 Dec 1994
Firstpage
159
Lastpage
164
Abstract
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
Keywords
cellular neural nets; computational linguistics; learning (artificial intelligence); Vapnik-Chervonenkis dimension; computational learning theory; discrete-time cellular neural networks; operation modes; probably approximately correct learning; upper bound; Cellular neural networks; Circuit synthesis; Computer networks; Extraterrestrial measurements; Neural networks; Nonhomogeneous media; Probability distribution; Testing; Training data; Upper bound;
fLanguage
English
Publisher
ieee
Conference_Titel
Cellular Neural Networks and their Applications, 1994. CNNA-94., Proceedings of the Third IEEE International Workshop on
Conference_Location
Rome
Print_ISBN
0-7803-2070-0
Type
conf
DOI
10.1109/CNNA.1994.381691
Filename
381691
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