  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.
