Ensembles of Kernel Predictors
This work addresses kernel selection and combination in machine learning, offering an incremental improvement over prior methods like learning kernel techniques.
The paper tackles the problem of learning with a finite set of base kernels by analyzing ensembles of kernel predictors, providing theoretical guarantees based on Rademacher complexity, introducing a learning algorithm, and conducting experiments on multiple datasets to compare results with existing kernel learning techniques.
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.