Meta-Parameters of Kernel Methods and Their Optimization
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F14%3A00432490" target="_blank" >RIV/67985807:_____/14:00432490 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Meta-Parameters of Kernel Methods and Their Optimization
Original language description
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. Theproposed methodology is demonstrated on experiments using benchmark data sets.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
<a href="/en/project/LD13002" target="_blank" >LD13002: Modeling of complex systems for softcomputing methods</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
ITAT 2014. Information Technologies - Applications and Theory. Part II
ISBN
978-80-87136-19-5
ISSN
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e-ISSN
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Number of pages
7
Pages from-to
99-105
Publisher name
Institute of Computer Science AS CR
Place of publication
Prague
Event location
Demänovská dolina
Event date
Sep 25, 2014
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
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