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