A meta-learning method to select the kernel width in Support Vector Regression

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This publication doesn't include Faculty of Economics and Administration. It includes Faculty of Informatics. Official publication website can be found on muni.cz.
Authors

SOARES Carlos BRAZDIL Pavel KUBA Petr

Year of publication 2004
Type Article in Periodical
Magazine / Source Machine Learning Journal
MU Faculty or unit

Faculty of Informatics

Citation
Web http://ipsapp009.kluweronline.com/IPS/frames/toc.aspx?J=4984&I=140
Field Informatics
Keywords meta-learning; parameter setting; support vector machines; Gaussian kernel; learning rankings
Description The Support Vector Machine algorithm is sensitive to the choice of parameter settings. If these are not set correctly, the algorithm may have a substandard performance. Suggesting a good setting is thus an important problem. We propose a meta-learning methodology for this purpose and exploit information about the past performance of different settings. The methodology is applied to set the width of the Gaussian kernel. We carry out an extensive empirical evaluation, including comparisons with other methods (fixed default ranking; selection based on cross-validation and a heuristic method commonly used to set the width of the SVM kernel). We show that our methodology can select settings with low error while providing significant savings in time. Further work should be carried out to see how the methodology could be adapted to different parameter setting tasks.
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