KUBA, Petr, Pavel BRAZDIL, Carlos SOARES and Adam WOZNICA. Exploiting Sampling and Meta-learning for Parameter Setting for Support Vector Machines. In Proc. of Workshop Learning and Data Mining associated with Iberamia 2002, VIII Iberoamerican Conference on Artificial Intellignce. Sevilla (Spain): University of Sevilla, 2002. p. 209-216.
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Basic information
Original name Exploiting Sampling and Meta-learning for Parameter Setting for Support Vector Machines
Authors KUBA, Petr (203 Czech Republic, guarantor), Pavel BRAZDIL (826 United Kingdom), Carlos SOARES (620 Portugal) and Adam WOZNICA (616 Poland).
Edition Sevilla (Spain), Proc. of Workshop Learning and Data Mining associated with Iberamia 2002, VIII Iberoamerican Conference on Artificial Intellignce, p. 209-216, 2002.
Publisher University of Sevilla
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher Czech Republic
Confidentiality degree is not subject to a state or trade secret
RIV identification code RIV/00216224:14330/02:00006718
Organization unit Faculty of Informatics
Keywords in English SVM; Meta-learning; parameter setting
Tags meta-learning, parameter setting, SVM
Changed by Changed by: RNDr. Petr Kuba, Ph.D., učo 3933. Changed: 27/5/2004 12:08.
Abstract
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 its parameters. 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 in experimentation time.
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MSM 143300003, plan (intention)Name: Interakce člověka s počítačem, dialogové systémy a asistivní technologie
Investor: Ministry of Education, Youth and Sports of the CR, Research Intents
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