A unifying view of multiple kernel learning
Kloft, Marius, Rückert, Ulrich, & Bartlett, Peter L. (2010) A unifying view of multiple kernel learning. Machine Learning and Knowledge Discovery in Databases, 6322, pp. 66-81.
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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|Item Type:||Journal Article|
|Additional Information:||Machine Learning and Knowledge Discovery in Databases European Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010, Proceedings, Part II|
|Keywords:||multiple kernel learning, risk minimization, Rademacher complexity bound|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100)|
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Science and Technology
Past > Schools > Mathematical Sciences
|Copyright Owner:||Copyright 2010 Springer|
|Deposited On:||18 Aug 2011 00:09|
|Last Modified:||29 Feb 2012 14:34|
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