A new scheme to learn a kernel in regularization networks

In this paper, we propose a new scheme to learn a kernel function from the convex combination of finite given kernels in regularization networks. We show that the corresponding variational problem is convex and under certain conditions, the variational problem can be approximated by a semidefinite programming problem which coincides with the Micchelli and Pontil's (MP's) Model (Micchelli and Pontil, 2005 [10]).