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Evolutionary learning of regularization networks with product kernel units

SYSNO ASEP	0369174
Document Type	C - Proceedings Paper (int. conf.)
R&D Document Type	Conference Paper
Title	Evolutionary learning of regularization networks with product kernel units
Author(s)	Vidnerová, Petra (UIVT-O)_{RID, SAI, ORCID} Neruda, Roman (UIVT-O)_{SAI, RID, ORCID}
Source Title	Systems, Man and Cybernetics. - Piscataway : IEEE, 2011 - ISSN 1062-922X - ISBN 978-1-4577-0652-3
Pages	s. 638-643
Number of pages	6 s.
Action	SMC 2011. International Conference on Systems, Man and Cybernetics
Event date	09.10.2011-12.10.2011
VEvent location	Anchorage
Country	US - United States
Event type	WRD
Language	eng - English
Country	US - United States
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)
	KJB100300804 GA AV ČR - Academy of Sciences of the Czech Republic (AV ČR)
CEZ	AV0Z10300504 - UIVT-O (2005-2011)
UT WOS	000298615101031
EID SCOPUS	83755183830
DOI	10.1109/ICSMC.2011.6083783
Annotation	This paper deals with learning possibilities of regularization networks with product kernel units. Approximation problems formulated as regularized minimization problems with kernel-based stabilizers lead to solutions of the shape of linear combination of kernel functions. These can be expressed as one-hidden layer feed-forward neural network schemes, called regularization networks. We propose a novel evolutionary algorithm utilizing for regularization networks with product kernels. This algorithm utilizes genetic search for suitable network parameters as well as kernel functions.
Workplace	Institute of Computer Science
Contact	Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800
Year of Publishing	2012

Number of the records: 1