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The success of kernel-based learning methods depend on the choice of kernel.Recently, kernel learning methods have been proposed that use data to selectthe most appropriate kernel, usually by combining a set of base kernels. Weintroduce a new algorithm for kernel learning that combines a {\em continuousset of base kernels}, without the common step of discretizing the space of basekernels. We demonstrate that our new method achieves state-of-the-artperformance across a variety of real-world datasets. Furthermore, we explicitlydemonstrate the importance of combining the right dictionary of kernels, whichis problematic for methods based on a finite set of base kernels chosen apriori. Our method is not the first approach to work with continuouslyparameterized kernels. However, we show that our method requires substantiallyless computation than previous such approaches, and so is more amenable tomultiple dimensional parameterizations of base kernels, which we demonstrate.

Alignment Based Kernel Learning with a Continuous Set of Base Kernels