Authors
Quoc Le, Tamás Sarlós, Alex Smola
Publication date
2013/6
Journal
Proceedings of the international conference on machine learning
Description
Abstract Despite their successes, what makes kernel methods difficult to use in many large
scale problems is the fact that computing the decision function is typically expensive,
especially at prediction time. In this paper, we overcome this difficulty by proposing Fastfood,
an approximation that accelerates such computation significantly. Key to Fastfood is the
observation that Hadamard matrices when combined with diagonal Gaussian matrices
exhibit properties similar to dense Gaussian random matrices. Yet unlike the latter, ...
scale problems is the fact that computing the decision function is typically expensive,
especially at prediction time. In this paper, we overcome this difficulty by proposing Fastfood,
an approximation that accelerates such computation significantly. Key to Fastfood is the
observation that Hadamard matrices when combined with diagonal Gaussian matrices
exhibit properties similar to dense Gaussian random matrices. Yet unlike the latter, ...
Total citations
Scholar articles
Q Le, T Sarlós, A Smola - Proceedings of the international conference on …, 2013
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