Name
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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-15883-4_5 | ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II |
Keywords | DocType | Volume |
dual representation,rademacher complexity,multiple kernel learning,general smooth optimization algorithm,unifying optimization criterion,existing formulation,multiple kernel,generalization error,framework analytically,unifying view,different formulation | Conference | abs/1005.0437 |
ISSN | ISBN | Citations |
0302-9743 | 3-642-15882-X | 25 |
PageRank | References | Authors |
0.96 | 18 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marius Kloft | 1 | 402 | 35.48 |
| U. Rückert | 2 | 755 | 103.61 |
| Peter L. Bartlett | 3 | 5482 | 1039.97 |