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Speeding Up Budgeted Stochastic Gradient Descent SVM Training with Precomputed Golden Section Search

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Machine Learning, Optimization, and Data Science (LOD 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11331))

Abstract

Limiting the model size of a kernel support vector machine to a pre-defined budget is a well-established technique that allows to scale SVM learning and prediction to large-scale data. Its core addition to simple stochastic gradient training is budget maintenance through merging of support vectors. This requires solving an inner optimization problem with an iterative method many times per gradient step. In this paper we replace the iterative procedure with a fast lookup. We manage to reduce the merging time by up to \(65\%\) and the total training time by \(44\%\) without any loss of accuracy.

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Notes

  1. 1.

    https://www.ini.rub.de/the_institute/people/tobias-glasmachers/#software .

  2. 2.

    Note that with increasing number of passes (or epochs) the standard deviation does not tend to zero since the training problem is non-convex due to the budget constraint.

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Acknowledgments

We acknowledge support by the Deutsche Forschungsgemeinschaft (DFG) through grant GL 839/3-1.

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Authors and Affiliations

  1. Institute for Neural Computation, Ruhr University Bochum, 44801, Bochum, NRW, Germany

    Tobias Glasmachers & Sahar Qaadan

Authors
  1. Tobias Glasmachers
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  2. Sahar Qaadan
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Corresponding author

Correspondence to Tobias Glasmachers .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Italy and University of Reading, Reading, UK

    Giuseppe Nicosia

  2. University of Florida, Gainesville, FL, USA

    Panos Pardalos

  3. University of Catania, Catania, Italy

    Giovanni Giuffrida

  4. Harvard University, Cambridge, MA, USA

    Renato Umeton

  5. IBM, Tivoli Research Lab, Rome, Italy

    Vincenzo Sciacca

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Glasmachers, T., Qaadan, S. (2019). Speeding Up Budgeted Stochastic Gradient Descent SVM Training with Precomputed Golden Section Search. In: Nicosia, G., Pardalos, P., Giuffrida, G., Umeton, R., Sciacca, V. (eds) Machine Learning, Optimization, and Data Science. LOD 2018. Lecture Notes in Computer Science(), vol 11331. Springer, Cham. https://doi.org/10.1007/978-3-030-13709-0_28

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  • DOI: https://doi.org/10.1007/978-3-030-13709-0_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-13708-3

  • Online ISBN: 978-3-030-13709-0

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