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14

ML
2007
ACM

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Feature space perspectives for learning the kernel

13 years 4 months ago

Feature space perspectives for learning the kernel

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In this paper, we continue our study of learning an optimal kernel in a prescribed convex set of kernels, [18]. 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 Lp regularization. 1 This work was supported by NSF Grant ITR-0312113, EPSRC Grant GR/T18707/01 and by the IST Programme of the European Community, under the PASCAL Network of Excellence IST-2002-506778.

Charles A. Micchelli, Massimiliano Pontil

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Added	27 Dec 2010
Updated	27 Dec 2010
Type	Journal
Year	2007
Where	ML
Authors	Charles A. Micchelli, Massimiliano Pontil

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