Number of the records: 1
Genetic Algorithm with Species for Regularization Network Metalearning
- 1.
SYSNO ASEP 0356026 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Genetic Algorithm with Species for Regularization Network Metalearning Author(s) Neruda, Roman (UIVT-O) SAI, RID, ORCID
Vidnerová, Petra (UIVT-O) RID, SAI, ORCIDSource Title Advances in Information Technology. - Berlin : Springer, 2010 / Papasratorn B.- ; Lavangnananda K. ; Chutimaskul W. ; Vanijja V. - ISSN 1865-0929 - ISBN 978-3-642-16698-3 Pages s. 192-201 Number of pages 10 s. Action IAIT 2010. International Conference on Advances in Information Technology /4./ Event date 04.11.2010-05.11.2010 VEvent location Bangkok Country TH - Thailand Event type WRD Language eng - English Country DE - Germany Keywords regularization ; neural networks ; metalearning ; genetic algorithms 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 000288365600021 EID SCOPUS 78650077194 DOI 10.1007/978-3-642-16699-0_21 Annotation Regularization networks are one of the important methods for supervised learning. They benefit from very good theoretical background, though their drawback is the presence of metaparameters. The metaparameters are typically supposed to be given by an user. In this paper, we develop a method for finding optimal values for metaparameters, namely type of kernel function, kernel’s parameter and regularization parameter. The method is based on genetic algorithms with different species for different kinds of kernels. The method is demonstrated on experiments. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2011
Number of the records: 1