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2007 International Conference on Computational Intelligence and Security (CIS 2007)
A Smoothing Support Vector Machine Based on Quarter Penalty Function
Harbin, Heilongjiang, China
December 15-December 19
ISBN: 0-7695-3072-9
| ASCII Text | x | ||
| Min Jiang, Zhiqing Meng, Gengui Zhou, "A Smoothing Support Vector Machine Based on Quarter Penalty Function," 2012 Eighth International Conference on Computational Intelligence and Security, pp. 57-59, 2007 International Conference on Computational Intelligence and Security (CIS 2007), 2007. | |||
| BibTex | x | ||
| @article{ 10.1109/CIS.2007.92, author = {Min Jiang and Zhiqing Meng and Gengui Zhou}, title = {A Smoothing Support Vector Machine Based on Quarter Penalty Function}, journal ={2012 Eighth International Conference on Computational Intelligence and Security}, volume = {0}, year = {2007}, isbn = {0-7695-3072-9}, pages = {57-59}, doi = {http://doi.ieeecomputersociety.org/10.1109/CIS.2007.92}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - 2012 Eighth International Conference on Computational Intelligence and Security TI - A Smoothing Support Vector Machine Based on Quarter Penalty Function SN - 0-7695-3072-9 SP57 EP59 A1 - Min Jiang, A1 - Zhiqing Meng, A1 - Gengui Zhou, PY - 2007 VL - 0 JA - 2012 Eighth International Conference on Computational Intelligence and Security ER - | |||
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CIS.2007.92
It is very important to find out a smoothing support vec- tor machine. This paper studies a smoothing support vec- tor machine (SVM) by using quarter penalty function. We introduce the optimization problem of SVM with an uncon- strained and nonsmooth optimization problem via quarter penalty function. Then, we define a one-order differentiable function to approximately smooth the penalty function, and get an unconstrained and smooth optimization problem. By error analysis, we may obtain approximate solution of SVM by solving its approximately smooth penalty optimization problem without constraints. The numerical experiment shows that our smoothing SVM is efficient.
Citation:
Min Jiang, Zhiqing Meng, Gengui Zhou, "A Smoothing Support Vector Machine Based on Quarter Penalty Function," cis, pp.57-59, 2007 International Conference on Computational Intelligence and Security (CIS 2007), 2007
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