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Learning the kernel with hyperkernels

Ong, Cheng Soon; Smola, Alexander; Williamson, Robert


This paper addresses the problem of choosing a kernel suitable for estimation with a support vector machine, hence further automating machine learning. This goal is achieved by defining a reproducing kernel Hilbert space on the space of kernels itself. Such a formulation leads to a statistical estimation problem similar to the problem of minimizing a regularized risk functional. We state the equivalent representer theorem for the choice of kernels and present a semidefinite programming...[Show more]

CollectionsANU Research Publications
Date published: 2005-07
Type: Journal article
Source: Journal of Machine Learning Research


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