Ensembles of Kernel Predictors

Details Ensembles of Kernel Predictors Ensembles of Kernel Predictors

2012-02-14

arxiv.org/abs/1202.3712v1

Computer Science

Learning

Scientific paper

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.

Affiliated with

Also associated with

No associations

Rating

Ensembles of Kernel Predictors does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Ensembles of Kernel Predictors, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Ensembles of Kernel Predictors will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-90397