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Feature space perspectives for learning the kernel

Micchelli, CA; Pontil, M; (2007) Feature space perspectives for learning the kernel. MACH LEARN , 66 (2-3) 297 - 319. 10.1007/s10994-006-0679-0.

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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.

Type: Article
Title: Feature space perspectives for learning the kernel
DOI: 10.1007/s10994-006-0679-0
Keywords: Banach space regularization, convex optimization, learning the kernels, kernel methods, sparsity, SELECTION
URI: http://discovery.ucl.ac.uk/id/eprint/163478
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