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We present Merged-Averaged Classifiers via Hashing (MACH) for K-classification with ultra-large values of K.
Universal classes of hash functions
J Lawrence Carter and Mark N Wegman · 1977
Earlier work this paper cites.
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Thomas G Dietterich and Ghulum Bakiri · 1995
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An improved data stream summary: the count-min sketch and its applications
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Compressing neural networks with the hashing trick
Wenlin Chen, James Wilson, Stephen Tyree, Kilian Weinberger, and Yixin Chen · 2015
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Song Han, Huizi Mao, and William J Dally · 2015
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