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Recent years have demonstrated that using random feature maps can significantly decrease the training and testing times of kernel-based algorithms without significantly lowering their accuracy.
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Fast and scalable polynomial kernels via explicit feature maps
N. Pham and R. Pagh · 2013
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M. Kocaoglu, K. Shanmugam, A. G. Dimakis, and A. Klivans · 2014
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Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels
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Random laplace feature maps for semigroup kernels on histograms
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Learning sparse polynomial functions
A. Andoni, R. Panigrahy, G. Valiant, and L. Zhang · 2014
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J. Yang, V. Sindhwani, Q. Fan, H. Avron, and M. Mahoney · 2014
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À la carte — learning fast kernels
Z. Yang, A. J. Smola, L. Song, and A. G. Wilson · 2015
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Compact Nonlinear Maps and Circulant Extensions
F. X. Yu, S. Kumar, H. Rowley, and S.-F. Chang · 2015
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