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Genetic Algorithm with Species for Regularization Network Metalearning
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SYSNO ASEP 0348394 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Genetic Algorithm with Species for Regularization Network Metalearning Author(s) Vidnerová, Petra (UIVT-O) RID, SAI, ORCID
Neruda, Roman (UIVT-O) SAI, RID, ORCIDSource Title Informačné Technológie - Aplikácie a Teória. - Seňa : Pont, 2010 / Pardubská D. - ISBN 978-80-970179-3-4 Pages s. 111-116 Number of pages 6 s. Action ITAT 2010. Conference on Theory and Practice of Information Technologies Event date 21.09.2010-25.09.2010 VEvent location Smrekovica Country SK - Slovakia Event type EUR Language eng - English Country SK - Slovakia Keywords regularization networks ; kernel functions ; genetic algorithms Subject RIV IN - Informatics, Computer Science R&D Projects KJB100300804 GA AV ČR - Academy of Sciences of the Czech Republic (AV ČR) CEZ AV0Z10300504 - UIVT-O (2005-2011) Annotation Regularization networks are one of the important methods for supervised learning. They benefit from very good theoretical background, although the presence of metaparameters is their drawback. The metaparameters are typically supposed to be given in advance and come ready as an input of the algorithm. Typically, they are set based on the task context by an experienced user. In this paper, we develop a method for finding optimal values of metaparameters, namely the type of kernel function, kernel parameters and regularization parameter. The method is based on co-evolutionary genetic algorithms with different species for different kind Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2011
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