An Improved Algorithm for Parallelizing Sequential Minimal Optimization

DOI

10.2991/itms-15.2015.331How to use a DOI?

Keywords

SVM; SMO; parallel algorithm; convergence

Abstract

In our previous work, a parallelizing sequential minimization optimization was proposed, where the algorithm was executed successfully but its convergence cannot be guaranteed in some cases. In this paper, an improved version is proposed, which can avoid falling into the endless loops. In the proposed method, the multiple violation pairs are selected in each step, and depending on the decrement value of the objective function, a single-pair update or multiple-pair update is determined. Experimental results show that the proposed method is more effective than the previous methods. The parallel algorithm is well executed while the accuracy is maintained and the convergence is completely guaranteed.

Copyright

© 2015, 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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TY  - CONF
AU  - C.R. Li
AU  - J. Guo
PY  - 2015/11
DA  - 2015/11
TI  - An Improved Algorithm for Parallelizing Sequential Minimal Optimization
BT  - Proceedings of the 2015 International Conference on Industrial Technology and Management Science
PB  - Atlantis Press
SP  - 1352
EP  - 1355
SN  - 2352-538X
UR  - https://doi.org/10.2991/itms-15.2015.331
DO  - 10.2991/itms-15.2015.331
ID  - Li2015/11
ER  -