Exploiting Sampling and Meta-learning for Parameter Setting for Support Vector Machines

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Authors	KUBA Petr BRAZDIL Pavel SOARES Carlos WOZNICA Adam
Year of publication	2002
Type	Article in Proceedings
Conference	Proc. of Workshop Learning and Data Mining associated with Iberamia 2002, VIII Iberoamerican Conference on Artificial Intellignce
MU Faculty or unit	Faculty of Informatics
Citation
Field	Informatics
Keywords	SVM; Meta-learning; parameter setting
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 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.
Related projects:	Human-computer interaction, dialog systems and assistive technologies