Learning the kernel with hyperkernels
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]
|Collections||ANU Research Publications|
|Source:||Journal of Machine Learning Research|
|Ong_Learning2005.pdf||424.58 kB||Adobe PDF|
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