A unifying view of multiple kernel learning

Kloft, Marius, Rückert, Ulrich, & Bartlett, Peter L. (2010) A unifying view of multiple kernel learning. Technical Report, UCB/EECS-2010-49. University of California, Berkeley.

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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 general 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.

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6 citations in Web of Science®
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ID Code: 44019
Item Type: Report
Refereed: No
Additional Information: Fulltext freely available see link above
Additional URLs:
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Past > Schools > Mathematical Sciences
Copyright Owner: Copyright 2010 please consult the authors
Deposited On: 18 Aug 2011 00:27
Last Modified: 01 Nov 2011 02:46

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