Design and Implementation of a Communication-Optimal Classifier for Distributed Kernel Support Vector Machines

We consider the problem of how to design and implement communication-efficient versions of parallel kernel 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, i...

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Veröffentlicht in:	IEEE transactions on parallel and distributed systems Jg. 28; H. 4; S. 974 - 988
Hauptverfasser:	You, Yang, Demmel, James, Czechowski, Kent, Song, Le, Vuduc, Rich
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.04.2017 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Classifiers Communication communication-avoidance Computer Science Data models Distributed memory Distributed memory algorithms Engineering Kernel Kernels Machine learning Mathematical analysis Matrix algebra Matrix methods Optimization Partitioning algorithms Processors Program processors Scaling Source code State of the art statistical machine learning Statistical models Supercomputers Support vector machines Training
ISSN:	1045-9219, 1558-2183
Online-Zugang:	Volltext
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