Machine Learning and Knowledge Discovery in Databases

Volume 6322 of the series Lecture Notes in Computer Science pp 66-81

A Unifying View of Multiple Kernel Learning

  • Marius KloftAffiliated withUniversity of California
  • , Ulrich RückertAffiliated withUniversity of California
  • , Peter L. BartlettAffiliated withUniversity of California

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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 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.