Exploiting Sampling and Meta-learning for Parameter Setting for Support Vector Machines
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F02%3A00006718" target="_blank" >RIV/00216224:14330/02:00006718 - 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
Exploiting Sampling and Meta-learning for Parameter Setting for Support Vector Machines
Original language description
It is a known fact that good parameter settings affect the performance of many machine learning algorithms. Support Vector Machines (SVM) and Neural Networks are particularly affected. In this paper, we concentrate on SVM and discuss some ways to set itsparameters. The first approach uses small samples, while the second one exploits meta-learning and past results. Both methods have been thoroughly evaluated. We show that both approaches enable us to obtain quite good results with significant savings inexperimentation time.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
BD - Information theory
OECD FORD branch
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Result continuities
Project
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Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)
Others
Publication year
2002
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proc. of Workshop Learning and Data Mining associated with Iberamia 2002, VIII Iberoamerican Conference on Artificial Intellignce
ISBN
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ISSN
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e-ISSN
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Number of pages
8
Pages from-to
209
Publisher name
University of Sevilla
Place of publication
Sevilla (Spain)
Event location
12. - 15. 11. 2002, Sevilla
Event date
Jan 1, 2002
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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