Feature space perspectives for learning the kernel.
297 - 319.
In this paper, we continue our study of learning an optimal kernel in a prescribed convex set of kernels (Micchelli & Pontil, 2005). We present a reformulation of this problem within a feature space environment. This leads us to study regularization in the dual space of all continuous functions on a compact domain with values in a Hilbert space with a mix norm. We also relate this problem in a special case to L-p regularization.
|Title:||Feature space perspectives for learning the kernel|
|Keywords:||Banach space regularization, convex optimization, learning the kernels, kernel methods, sparsity, SELECTION|
|UCL classification:||UCL > School of BEAMS
UCL > School of BEAMS > Faculty of Engineering Science
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