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Feature space perspectives for learning the kernel
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DOI：10.1007/s10994-006-0679-0.png)
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Abstract
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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.
Keywords:
Banach space regularization
convex optimization
learning the kernels
kernel methods
sparsity
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2.9
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