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Evolving Sum and Composite Kernel Functions for Regularization Networks
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SYSNO ASEP 0359155 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Evolving Sum and Composite Kernel Functions for Regularization Networks Author(s) Vidnerová, Petra (UIVT-O) RID, SAI, ORCID
Neruda, Roman (UIVT-O) SAI, RID, ORCIDSource Title Adaptive and Natural Computing Algorithms. Part I, 1. - Heidelberg : Springer, 2011 / Dobnikar A. ; Lotrič U. ; Šter B. - ISSN 0302-9743 - ISBN 978-3-642-20281-0 Pages s. 180-189 Number of pages 10 s. Action ICANNGA'2011. International Conference /10./ Event date 14.04.2011-16.04.2011 VEvent location Ljubljana Country SI - Slovenia Event type WRD Language eng - English Country DE - Germany Keywords regularization networks ; kernel functions ; genetic algorithms Subject RIV IN - Informatics, Computer Science R&D Projects OC10047 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) KJB100300804 GA AV ČR - Academy of Sciences of the Czech Republic (AV ČR) CEZ AV0Z10300504 - UIVT-O (2005-2011) UT WOS 000302389300019 EID SCOPUS 79955088766 DOI 10.1007/978-3-642-20282-7_19 Annotation In this paper we propose a novel evolutionary algorithm for regularization networks. The main drawback of regularization networks in practical applications is the presence of meta-parameters, including the type and parameters of kernel functions Our learning algorithm provides a solution to this problem by searching through a space of different kernel functions, including sum and composite kernels. Thus, an optimal combination of kernel functions with parameters is evolved for given task specified by training data. Comparisons of composite kernels, single kernels, and traditional Gaussians are provided in several experiments. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2012
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