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A unifying view of multiple kernel learning

Kloft, Marius, Rückert, Ulrich, & Bartlett, Peter L. (2010) A unifying view of multiple kernel learning. Machine Learning and Knowledge Discovery in Databases, 6322, pp. 66-81.

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Abstract

Recent research on multiple kernel learning has lead to a number of approaches for combining kernels in regularized risk minimization. The proposed approaches include different formulations of objectives and varying regularization strategies. In this paper we present a unifying optimization criterion for multiple kernel learning and show how existing formulations are subsumed as special cases. We also derive the criterion’s dual representation, which is suitable for general smooth optimization algorithms. Finally, we evaluate multiple kernel learning in this framework analytically using a Rademacher complexity bound on the generalization error and empirically in a set of experiments.

Impact and interest:

2 citations in Scopus
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2 citations in Web of Science®

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ID Code: 43972
Item Type: Journal Article
Additional Information: Machine Learning and Knowledge Discovery in Databases European Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010, Proceedings, Part II
Keywords: multiple kernel learning, risk minimization, Rademacher complexity bound
DOI: 10.1007/978-3-642-15883-4_5
ISBN: 9783540874782
ISSN: 0302-9743
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Past > Schools > Mathematical Sciences
Copyright Owner: Copyright 2010 Springer
Deposited On: 18 Aug 2011 10:09
Last Modified: 01 Mar 2012 00:34

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