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

• Type: Article in International Scientific Journal
• Date: 2004

Authors

Soares, C (Author) FEP; Brazdil, PB (Author) FEP; Kuba, P (Author) Other

Journal

Title: Machine Learning

Vol. 54

Pages: 195-209

ISSN: 0885-6125

Publisher: Springer Nature

Indexing

ISI Proceedings

ISI Web of Knowledge - 118 Citations

ISI Web of Science

Scopus - 151 Citations

Scientific classification

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: P-000-BFJ

DOI: 10.1023/b:mach.0000015879.28004.9b

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 15