Cheng Soon Ong and Alexander J. Smola
We expand on the problem of learning a kernel via a RKHS on the space of kernels itself. The resulting optimization problem is shown to have a semidefinite programming solution. We demonstrate that it is possible to learn the kernel for various formulations of machine learning problems. Specifically, we provide mathematical programming formulations and experimental results for the CSVM, ν-SVM and Lagrangian SVM for classification on UCI data, and novelty detection.