A Unifying View of Multiple Kernel Learning

  • Marius Kloft
  • Ulrich Rückert
  • Peter L. Bartlett
Conference paper

DOI: 10.1007/978-3-642-15883-4_5

Part of the Lecture Notes in Computer Science book series (LNCS, volume 6322)
Cite this paper as:
Kloft M., Rückert U., Bartlett P.L. (2010) A Unifying View of Multiple Kernel Learning. In: Balcázar J.L., Bonchi F., Gionis A., Sebag M. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2010. Lecture Notes in Computer Science, vol 6322. Springer, Berlin, Heidelberg

Abstract

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Marius Kloft
    • 1
  • Ulrich Rückert
    • 1
  • Peter L. Bartlett
    • 1
  1. 1.University of CaliforniaBerkeleyUSA

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