Proceedings of 2016 International Conference on Modeling, Simulation and Optimization Technologies and Applications (MSOTA2016)

Surrogate-Based Support Vector Machine Method

Authors
Yueyang Teng, Yaxin Liu, Xuan Xie, Bin Lu, Yan Kang
Corresponding Author
Yueyang Teng
Available Online December 2016.
DOI
https://doi.org/10.2991/msota-16.2016.66How to use a DOI?
Keywords
surrogate; Kuhn-Tucker (KT); conditions; regularization technique; sparsity/smoothness penalty
Abstract

Surrogate-based method (SBM) is used to train a support vector machine (SVM) for discriminating between the elements of two classes of input points. The key idea to develop the algorithm is to replace the minimization of the cost function at each iteration by the minimization of a surrogate function, leading to a guaranteed decrease in the cost function. SBM simultaneously update all of points, which is very different from Platt's sequential minimal optimization (SMO) and Joachims' SVM light. The former handles one point at a time and the latter handles a small number of points at a time. In contrast to the sequential methods, SBM is easy to parallelize. The proposed algorithm has some favorable properties, including the monotonic decrease of the cost function, the self-constraining in the feasible region, and the absence of a predetermined step size and any additional parameter. This paper theoretically proves that the iteration sequence will converge to a sole global solution. Encouraging numerical results are presented on data sets, and SBM provides a performance comparable with that of other commonly used methods as concerns convergence speed and computational cost.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of 2016 International Conference on Modeling, Simulation and Optimization Technologies and Applications (MSOTA2016)
Series
Advances in Computer Science Research
Publication Date
December 2016
ISBN
978-94-6252-284-8
ISSN
2352-538X
DOI
https://doi.org/10.2991/msota-16.2016.66How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Yueyang Teng
AU  - Yaxin Liu
AU  - Xuan Xie
AU  - Bin Lu
AU  - Yan Kang
PY  - 2016/12
DA  - 2016/12
TI  - Surrogate-Based Support Vector Machine Method
BT  - Proceedings of 2016 International Conference on Modeling, Simulation and Optimization Technologies and Applications (MSOTA2016)
PB  - Atlantis Press
SP  - 307
EP  - 314
SN  - 2352-538X
UR  - https://doi.org/10.2991/msota-16.2016.66
DO  - https://doi.org/10.2991/msota-16.2016.66
ID  - Teng2016/12
ER  -