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Distributed optimization of multi-class SVMs

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  • Maximilian Alber
  • Julian Zimmert
  • Urun Dogan
  • Marius Kloft

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.

Suggested Citation

  • Maximilian Alber & Julian Zimmert & Urun Dogan & Marius Kloft, 2017. "Distributed optimization of multi-class SVMs," PLOS ONE, Public Library of Science, vol. 12(6), pages 1-18, June.
    • Handle: RePEc:plo:pone00:0178161
    DOI: 10.1371/journal.pone.0178161
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    References listed on IDEAS

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    1. Lee, Yoonkyung & Lin, Yi & Wahba, Grace, 2004. "Multicategory Support Vector Machines: Theory and Application to the Classification of Microarray Data and Satellite Radiance Data," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 67-81, January.
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    1. Bandeh Ali Talpur & Declan O’Sullivan, 2020. "Cyberbullying severity detection: A machine learning approach," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-19, October.

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