Meta-Parameters of Kernel Methods and Their Optimization

0432490 - ÚI 2015 RIV CZ eng C - Konferenční příspěvek (zahraniční konf.)
Vidnerová, Petra - Neruda, Roman
Meta-Parameters of Kernel Methods and Their Optimization.
ITAT 2014. Information Technologies - Applications and Theory. Part II. Prague: Institute of Computer Science AS CR, 2014 - (Kůrková, V.; Bajer, L.; Peška, L.; Vojtáš, R.; Holeňa, M.; Nehéz, M.), s. 99-105. ISBN 978-80-87136-19-5.
[ITAT 2014. European Conference on Information Technologies - Applications and Theory /14./. Demänovská dolina (SK), 25.09.2014-29.09.2014]
Grant CEP: GA MŠMT(CZ) LD13002
Institucionální podpora: RVO:67985807
Klíčová slova: kernel methods * metalearning * computational intelligence
Kód oboru RIV: IN - Informatika

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.
Trvalý link: http://hdl.handle.net/11104/0236830