PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

The Local Rademacher Complexity of Lp-Norm Multiple Kernel Learning
Marius Kloft and Gilles Blanchard
In: Advances in Neural Information Processing Systems (2012) Neural Information Processing Systems , Vancouver, British Columbia, Canada .

Abstract

We derive an upper bound on the local Rademacher complexity of p-norm multiple kernel learning, which yields a tighter excess risk bound than global approaches. Previous local approaches analyzed the case p = 1 only while our analysis covers all cases, assuming the different feature mappings corresponding to the different kernels to be uncorrelated. We also show a lower bound that shows that the bound is tight, and derive consequences regarding excess loss, namely fast convergence rates.

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EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:9401
Deposited By:Marius Kloft
Deposited On:16 March 2012