DocumentCode :
2485543
Title :
A Novel Model of Working Set Selection for SMO Decomposition Methods
Author :
Zhao, Zhen-Dong ; Yuan, Lei ; Wang, Yu-Xuan ; Bao, Forrest Sheng ; Zhang, Shun-yi ; Sun, Yan-Fei
Author_Institution :
Nanjing Univ. of Posts & Telecommun., Nanjing
Volume :
2
fYear :
2007
fDate :
29-31 Oct. 2007
Firstpage :
283
Lastpage :
290
Abstract :
In the process of training support vector machines (SVMs) by decomposition methods, working set selection is an important technique, and some exciting schemes were employed into this field. To improve working set selection, we propose a new model for working set selection in sequential minimal optimization (SMO) decomposition methods. In this model, it selects B as working set without reselection. Some properties are given by simple proof, and experiments demonstrate that the proposed method is in general faster than existing methods.
Keywords :
optimisation; support vector machines; decomposition methods; sequential minimal optimization; support vector machines; working set selection; Artificial intelligence; Convergence; Kernel; Matrix decomposition; Optimization methods; Support vector machines; Testing; Upper bound;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Tools with Artificial Intelligence, 2007. ICTAI 2007. 19th IEEE International Conference on
Conference_Location :
Patras
ISSN :
1082-3409
Print_ISBN :
978-0-7695-3015-4
Type :
conf
DOI :
10.1109/ICTAI.2007.99
Filename :
4410393
Link To Document :
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