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Some Comparisons of Complexity in Dictionary-Based and Linear Computational Models
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SYSNO ASEP 0350421 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Some Comparisons of Complexity in Dictionary-Based and Linear Computational Models Author(s) Gnecco, G. (IT)
Kůrková, Věra (UIVT-O) RID, SAI, ORCID
Sanguineti, M. (IT)Source Title Neural Networks. - : Elsevier - ISSN 0893-6080
Roč. 24, č. 2 (2011), s. 171-182Number of pages 12 s. Language eng - English Country GB - United Kingdom Keywords linear approximation schemes ; variable-basis approximation schemes ; model complexity ; worst-case errors ; neural networks ; kernel models Subject RIV IN - Informatics, Computer Science R&D Projects GA201/08/1744 GA ČR - Czech Science Foundation (CSF) CEZ AV0Z10300504 - UIVT-O (2005-2011) UT WOS 000287910100004 EID SCOPUS 79251629004 DOI 10.1016/j.neunet.2010.10.002 Annotation We compare upper bounds on worst-case errors in variable-basis approximation with lower bounds on such errors for any linear approximator. Using methods from nonlinear approximation and integral representations tailored to computational units, we describe some cases where neural networks outperform any linear approximator. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2011
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