Original title: Meta-Parameters of Kernel Methods and Their Optimization
Authors: Vidnerová, Petra ; Neruda, Roman
Document type: Papers
Conference/Event: ITAT 2014. European Conference on Information Technologies - Applications and Theory /14./, Demänovská dolina (SK), 2014-09-25 / 2014-09-29
Year: 2014
Language: eng
Abstract: 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.
Keywords: computational intelligence; kernel methods; metalearning
Project no.: LD13002 (CEP)
Funding provider: GA MŠk
Host item entry: ITAT 2014. Information Technologies - Applications and Theory. Part II, ISBN 978-80-87136-19-5

Institution: Institute of Computer Science AS ČR (web)
Document availability information: Fulltext is available in the digital repository of the Academy of Sciences.
Original record: http://hdl.handle.net/11104/0236830

Permalink: http://www.nusl.cz/ntk/nusl-175470


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