Uncertainty in Artificial Intelligence
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Ensembles of Kernel Predictors
Corinna Cortes, Mehryar Mohri, Afshin Rostamizadeh
This paper examines the problem of learning with a finite and possibly large set of p base kernels. It presents a theoretical and empirical analysis of an approach addressing this problem based on ensembles of kernel predictors. This includes novel theoretical guarantees based on the Rademacher complexity of the corresponding hypothesis sets, the introduction and analysis of a learning algorithm based on these hypothesis sets, and a series of experiments using ensembles of kernel predictors with several data sets. Both convex combinations of kernel-based hypotheses and more general Lq-regularized nonnegative combinations are analyzed. These theoretical, algorithmic, and empirical results are compared with those achieved by using learning kernel techniques, which can be viewed as another approach for solving the same problem.
Pages: 145-152
PS Link:
PDF Link: /papers/11/p145-cortes.pdf
AUTHOR = "Corinna Cortes and Mehryar Mohri and Afshin Rostamizadeh",
TITLE = "Ensembles of Kernel Predictors",
BOOKTITLE = "Proceedings of the Twenty-Seventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-11)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2011",
PAGES = "145--152"

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