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Abstract

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 formulation of the resulting optimization
problem. Several recipes for constructing hyperkernels are provided, as
well as the details of common machine learning problems. Experimental
results for classification, regression and novelty
detection on UCI data show the feasibility of our approach.