New Generalization Bounds for Learning Kernels

Details New Generalization Bounds for Learning Kernels New Generalization Bounds for Learning Kernels

2009-12-17

arxiv.org/abs/0912.3309v1

Computer Science

Artificial Intelligence

Scientific paper

This paper presents several novel generalization bounds for the problem of learning kernels based on the analysis of the Rademacher complexity of the corresponding hypothesis sets. Our bound for learning kernels with a convex combination of p base kernels has only a log(p) dependency on the number of kernels, p, which is considerably more favorable than the previous best bound given for the same problem. We also give a novel bound for learning with a linear combination of p base kernels with an L_2 regularization whose dependency on p is only in p^{1/4}.

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