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Meta-Parameters of Kernel Methods and Their Optimization
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SYSNO ASEP 0432490 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Meta-Parameters of Kernel Methods and Their Optimization Author(s) Vidnerová, Petra (UIVT-O) RID, SAI, ORCID
Neruda, Roman (UIVT-O) SAI, RID, ORCIDSource Title ITAT 2014. Information Technologies - Applications and Theory. Part II. - Prague : Institute of Computer Science AS CR, 2014 / Kůrková V. ; Bajer L. ; Peška L. ; Vojtáš R. ; Holeňa M. ; Nehéz M. - ISBN 978-80-87136-19-5 Pages s. 99-105 Number of pages 7 s. Publication form Print - P Action ITAT 2014. European Conference on Information Technologies - Applications and Theory /14./ Event date 25.09.2014-29.09.2014 VEvent location Demänovská dolina Country SK - Slovakia Event type EUR Language eng - English Country CZ - Czech Republic Keywords kernel methods ; metalearning ; computational intelligence Subject RIV IN - Informatics, Computer Science R&D Projects LD13002 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) Institutional support UIVT-O - RVO:67985807 Annotation In this work we deal with the problem of metalearning for kernel based methods. Among the kernel methods we focus on the support vector machine (SVM), that have become a method of choice in a wide range of practical applications, and on the regularization network (RN) with a sound background in approximation theory. We discuss the role of kernel function in learning, and we explain several search methods for kernel function optimization, including grid search, genetic search and simulated annealing. The proposed methodology is demonstrated on experiments using benchmark data sets. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2015
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