Distributed optimization of multi-class SVMs

Maximilian Alber, Julian Zimmert, Urun Dogan and Marius Kloft

PLOS ONE, 2017, vol. 12, issue 6, 1-18

Abstract: Training of one-vs.-rest SVMs can be parallelized over the number of classes in a straight forward way. Given enough computational resources, one-vs.-rest SVMs can thus be trained on data involving a large number of classes. The same cannot be stated, however, for the so-called all-in-one SVMs, which require solving a quadratic program of size quadratically in the number of classes. We develop distributed algorithms for two all-in-one SVM formulations (Lee et al. and Weston and Watkins) that parallelize the computation evenly over the number of classes. This allows us to compare these models to one-vs.-rest SVMs on unprecedented scale. The results indicate superior accuracy on text classification data.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0178161

DOI: 10.1371/journal.pone.0178161

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