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arXiv · 1310.3101

Deep Multiple Kernel Learning

Eric Strobl·Shyam Visweswaran
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Abstract

Deep learning methods have predominantly been applied to large artificial neural networks. Despite their state-of-the-art performance, these large networks typically do not generalize well to datasets with limited sample sizes. In this paper, we take a different approach by learning multiple layers of kernels. We combine kernels at each layer and then optimize over an estimate of the support vector machine leave-one-out error rather than the dual objective function. Our experiments on a variety of datasets show that each layer successively increases performance with only a few base kernels.

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Eric Strobl, Shyam Visweswaran. 2013-10-11. Deep Multiple Kernel Learning. https://doi.org/10.1109/icmla.2013.84

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