Genetic Algorithm with Species for Regularization Network Metalearning
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F10%3A00356026" target="_blank" >RIV/67985807:_____/10:00356026 - 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
Genetic Algorithm with Species for Regularization Network Metalearning
Original language description
Regularization networks are one of the important methods for supervised learning. They benefit from very good theoretical background, though their drawback is the presence of metaparameters. The metaparameters are typically supposed to be given by an user. In this paper, we develop a method for finding optimal values for metaparameters, namely type of kernel function, kernel?s parameter and regularization parameter. The method is based on genetic algorithms with different species for different kinds ofkernels. The method is demonstrated on experiments.
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/GA201%2F08%2F1744" target="_blank" >GA201/08/1744: Complexity of perceptron and kernel networks</a><br>
Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)
Others
Publication year
2010
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Advances in Information Technology
ISBN
978-3-642-16698-3
ISSN
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e-ISSN
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Number of pages
10
Pages from-to
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Publisher name
Springer
Place of publication
Berlin
Event location
Bangkok,
Event date
Nov 4, 2010
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000288365600021