Sparse Recovery in Large Ensembles of Kernel Machines On-Line Learning and Bandits
نویسندگان
چکیده
A problem of learning a prediction rule that is approximated in a linear span of a large number of reproducing kernel Hilbert spaces is considered. The method is based on penalized empirical risk minimization with `1type complexity penalty. Oracle inequalities on excess risk of such estimators are proved showing that the method is adaptive to unknown degree of “sparsity” of the target function.
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تاریخ انتشار 2008