PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Unifying View of Multiple Kernel Learning
Marius Kloft, Ulrich Rückert and Peter L. Bartlett
In: ECML 2010, 20 Sep - 24 Sep, Barcelona, Spain.

Abstract

Recent research on multiple kernel learning has lead to a number of approaches for combining kernels in regularized risk mini- mization. The proposed approaches include dierent formulations of ob- jectives 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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:8087
Deposited By:Marius Kloft
Deposited On:18 April 2011