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Some Comparisons of Model Complexity in Linear and Neural-Network Approximation
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SYSNO ASEP 0345940 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Some Comparisons of Model Complexity in Linear and Neural-Network Approximation Author(s) Gnecco, G. (IT)
Kůrková, Věra (UIVT-O) RID, SAI, ORCID
Sanguineti, M. (IT)Source Title Artificial Neural Networks – ICANN 2010, 3. - Berlin : Springer, 2010 / Diamantaras K. ; Duch W. ; Iliadis L.S. - ISSN 0302-9743 - ISBN 978-3-642-15824-7 Pages s. 358-367 Number of pages 10 s. Action ICANN 2010. International Conference on Artificial Neural Networks /20./ Event date 15.09.2010-18.09.2010 VEvent location Thessaloniki Country GR - Greece Event type WRD Language eng - English Country DE - Germany Keywords model complexity ; neural networks ; linear models Subject RIV IN - Informatics, Computer Science R&D Projects OC10047 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) CEZ AV0Z10300504 - UIVT-O (2005-2011) UT WOS 000290245400048 EID SCOPUS 78049387383 DOI 10.1007/978-3-642-15825-4_48 Annotation Capabilities of linear and neural-network models are compared from the point of view of requirements on the growth of model complexity with an increasing accuracy of approximation. The bounds are formulated in terms of singular numbers of certain operators induced by computational units and high-dimensional volumes of the domains of the functions to be approximated. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2011
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