Learning with Kernel Based Regularization Methods

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SYSNO ASEP	0405548
Document Type	C - Proceedings Paper (int. conf.)
R&D Document Type	Conference Paper
Title	Learning with Kernel Based Regularization Methods
Title	Učení pomocí jádrových regularizačních sítí
Author(s)	Kudová, Petra (UIVT-O)_{SAI, RID, ORCID}
Source Title	ITAT 2005. Information Technologies - Applications and Theory. - Košice : Prírodovedecká fakulta, Univerzita P. J. Šafárika, 2005 / Vojtáš P. - ISBN 80-7097-609-8
Pages	s. 83-92
Number of pages	10 s.
Action	ITAT 2005
Event date	20.09.2005-25.09.2005
VEvent location	Račkova dolina
Country	SK - Slovakia
Event type	EUR
Language	eng - English
Country	SK - Slovakia
Keywords	learning from examples ; regularization networks ; kernel methods
Subject RIV	BA - General Mathematics
R&D Projects	GA201/05/0557 GA ČR - Czech Science Foundation (CSF)
CEZ	AV0Z10300504 - UIVT-O (2005-2011)
EID SCOPUS	33746246993
Annotation	We discuss one approach to learning from examples - the kernel based regularization networks, with the focus on its practical aspects and applicability on real tasks. We describe techniques for estimation of explicit parameters of this method. Performance of described algorithms is demonstrated on experiments.
Workplace	Institute of Computer Science
Contact	Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800
Year of Publishing	2006

Number of the records: 1