Hyperkernels
We consider the problem of choosing a kernel suitable for est imation using a Gaussian Process estimator or a Support Vector Machi ne. A novel solution is presented which involves defining a Reprod ucing Kernel Hilbert Space on the space of kernels itself. By utilizin g an analog of the classical representer theorem, the problem of choosi ng a kernel from a parameterized family of kernels (e.g. of varying widt h) is reduced to a statistical estimation problem akin to the problem of mi ni izing a regularized risk functional. Various classical settings f or model or kernel selection are special cases of our framework.