Kernel methods match Deep Neural Networks on TIMIT
Published: May 1, 2014
Abstract
Despite their theoretical appeal and grounding in tractable convex optimization techniques, kernel methods are often not the first choice for large-scale speech applications due to their significant memory requirements and computational expense. In recent years, randomized approximate feature maps have emerged as an elegant mechanism to scale-up kernel methods. Still, in practice, a large number of random features is required to obtain...
Paper Details
Title
Kernel methods match Deep Neural Networks on TIMIT
Published Date
May 1, 2014
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