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A DC-programming algorithm for kernel selection

Abstract:

We address the problem of learning a kernel for a given supervised learning task. Our approach consists in searching within the convex hull of a prescribed set of basic kernels for one which minimizes a convex regularization functional. A unique feature of this approach compared to others in the literature is that the number of basic kernels can be infinite. We only require that they are continuously parameterized. For example, the basic kernels could be isotropic Gaussians with variance in a...

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Publication date:
2006-06-05
URN:
uuid:dee66c6c-04ed-44e6-88f5-2b518642189e
Local pid:
oai:eprints.maths.ox.ac.uk:1115

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