Selecting parameters of SVM using meta-learning and kernel matrix-based meta-features

Authors:
Carlos Soares;Pavel B. Brazdil
Affiliations:
University of Porto, Porto, Portugal;University of Porto, Porto, Portugal
Venue:
Proceedings of the 2006 ACM symposium on Applied computing
Year:
2006

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Abstract

The Support Vector Machine (SVM) algorithm is sensitive to the choice of parameter settings, which makes it hard to use by non-experts. It has been shown that meta-learning can be used to support the selection of SVM parameter values. Previous approaches have used general statistical measures as meta-features. Here we propose a new set of meta-features that are based on the kernel matrix. We test them on the problem of setting the width of the Gaussian kernel for regression problems. We obtain significant improvements in comparison to earlier meta-learning results. We expect that with better support in the selection of parameter values, SVM becomes accessible to a wider range of users.