nips nips2002 nips2002-106 nips2002-106-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Cheng S. Ong, Robert C. Williamson, Alex J. Smola
Abstract: We consider the problem of choosing a kernel suitable for estimation using a Gaussian Process estimator or a Support Vector Machine. A novel solution is presented which involves defining a Reproducing Kernel Hilbert Space on the space of kernels itself. By utilizing an analog of the classical representer theorem, the problem of choosing a kernel from a parameterized family of kernels (e.g. of varying width) is reduced to a statistical estimation problem akin to the problem of minimizing a regularized risk functional. Various classical settings for model or kernel selection are special cases of our framework.
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