Margin and Radius Based Multiple Kernel Learning

Authors:
Huyen Do;Alexandros Kalousis;Adam Woznica;Melanie Hilario
Affiliations:
Computer Science Department, University of Geneva, Carouge, Switzerland 1227;Computer Science Department, University of Geneva, Carouge, Switzerland 1227;Computer Science Department, University of Geneva, Carouge, Switzerland 1227;Computer Science Department, University of Geneva, Carouge, Switzerland 1227
Venue:
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Year:
2009

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Abstract

A serious drawback of kernel methods, and Support Vector Machines (SVM) in particular, is the difficulty in choosing a suitable kernel function for a given dataset. One of the approaches proposed to address this problem is Multiple Kernel Learning (MKL) in which several kernels are combined adaptively for a given dataset. Many of the existing MKL methods use the SVM objective function and try to find a linear combination of basic kernels such that the separating margin between the classes is maximized. However, these methods ignore the fact that the theoretical error bound depends not only on the margin, but also on the radius of the smallest sphere that contains all the training instances. We present a novel MKL algorithm that optimizes the error bound taking account of both the margin and the radius. The empirical results show that the proposed method compares favorably with other state-of-the-art MKL methods.