CA-SVM: Communication-Avoiding Support Vector Machines on Distributed Systems

We consider the problem of how to design and implement communication-efficient versions of parallel support vector machines, a widely used classifier in statistical machine learning, for distributed memory clusters and supercomputers. The main computational bottleneck is the training phase, in which...

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Vydané v:	Proceedings - IEEE International Parallel and Distributed Processing Symposium s. 847 - 859
Hlavní autori:	Yang You, Demmel, James, Czechowski, Kenneth, Le Song, Vuduc, Richard
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 01.05.2015
Predmet:	Accuracy communication avoidance distributed memory algorithms Kernel Mathematical model Partitioning algorithms Program processors statistical machine learning Support vector machines Training
ISSN:	1530-2075
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