Ong, Cheng Song; Smola, Alexander
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 C-SVM, v-SVM and Lagrangian SVM for classification on UCI data, and novelty detection.
Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.