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