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Evolutionary Learning of Regularization Networks with Multi-kernel Units
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SYSNO ASEP 0369159 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Evolutionary Learning of Regularization Networks with Multi-kernel Units Author(s) Vidnerová, Petra (UIVT-O) RID, SAI, ORCID
Neruda, Roman (UIVT-O) SAI, RID, ORCIDSource Title Advances in Neural Networks – ISNN 2011. Part I. - Berlin : Springer, 2011 / Liu D. ; Zhang H. ; Polycarpou M. ; Alippi C. ; He H. - ISSN 0302-9743 - ISBN 978-3-642-21104-1 Pages s. 538-546 Number of pages 9 s. Action ISNN 2011. International Symposium on Neural Networks /8./ Event date 29.05.2011-01.06.2011 VEvent location Guilin Country CN - China Event type WRD Language eng - English Country DE - Germany Keywords genetic algorithms ; kernel functions ; regularization networks Subject RIV IN - Informatics, Computer Science R&D Projects GAP202/11/1368 GA ČR - Czech Science Foundation (CSF) CEZ AV0Z10300504 - UIVT-O (2005-2011) UT WOS 000301802600062 EID SCOPUS 79957795865 DOI 10.1007/978-3-642-21105-8_62 Annotation Regularization networks represent an important supervised learning method applicable for regression and classification tasks. They benefit from very good theoretical background, although the presence of meta parameters is their drawback. The meta parameters, including the type of kernel function, are typically supposed to be given in advance and come ready as an input of the algorithm. In this paper, we propose multi-kernel functions, namely product kernel functions and composite kernel functions. The choice of kernel function becomes part of the optimization process, for which a new evolutionary learning algorithm is introduced that deals with different kernel functions, including composite kernels. The results are demonstrated on experiments with benchmark tasks. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2012
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