Deep Multiple Kernel Learning
Published: Dec 1, 2013
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...
Paper Details
Title
Deep Multiple Kernel Learning
Published Date
Dec 1, 2013
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