Model selection and estimation with multiple reproducing Karnel Hilbert spaces
Seminar Room 1, Newton Institute
In this talk, we consider the problem of learning a target function that belongs to the linear span of a large number of reproducing kernel Hilbert spaces. Such a problem arises naturally in many practice situations with the ANOVA, the additive model and multiple kernel learning as the most well known and important examples. We investigate approaches based on l1-type complexity regularization. We study the theoretical properties from both variable selection and estimation perspective. We establish several probabilistic inequalities providing bounds on the excess risk and L2-error that depend on the sparsity of the problem.
(part of the talk are based on joint work with Vladimir Koltchinskii.)
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