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CORR
2010
Springer

A Unifying View of Multiple Kernel Learning

10 years 8 months ago
A Unifying View of Multiple Kernel Learning
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.
Marius Kloft, Ulrich Rückert, Peter L. Bartle
Added 14 May 2011
Updated 14 May 2011
Type Journal
Year 2010
Where CORR
Authors Marius Kloft, Ulrich Rückert, Peter L. Bartlett
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