The goal of Multiple Kernel Learning (MKL) is to combine kernels derived from multiple sources in a data-driven way with the aim to enhance the accuracy of a kernel based machine. In this paper, we propose a time and space ecient MKL algorithm that can easily cope with hundreds of thousands of kernels and more. We compared our algorithm with other baselines plus three state-of-the-art MKL methods showing that our approach is often superior.
Easy multiple kernel learning
AIOLLI, FABIO;DONINI, MICHELE
2014
Abstract
The goal of Multiple Kernel Learning (MKL) is to combine kernels derived from multiple sources in a data-driven way with the aim to enhance the accuracy of a kernel based machine. In this paper, we propose a time and space ecient MKL algorithm that can easily cope with hundreds of thousands of kernels and more. We compared our algorithm with other baselines plus three state-of-the-art MKL methods showing that our approach is often superior.
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