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Proceedings Paper

Using novel support vector machines for efficient classification
Author(s): Yong Wang; Wei Zhang; Jun Chen; Li Xiao; Jianfu Li
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Paper Abstract

An Improved Support Vector Machines was proposed which starts with a small set and then sequentially expands to include feature space informative data points into the set. These feature space informative data points will be identified by solving a small least squares problem. The approach provides a mechanism to determine the set size automatically and dynamically and the set generated by this method will be more representative than the one by purely random selection. All advantages of SVM for dealing with nonlinear classification problem are retained.

Paper Details

Date Published: 9 January 2008
PDF: 6 pages
Proc. SPIE 6794, ICMIT 2007: Mechatronics, MEMS, and Smart Materials, 67944F (9 January 2008); doi: 10.1117/12.784051
Show Author Affiliations
Yong Wang, Chongqing Education College (China)
Wei Zhang, Chongqing Education College (China)
Jun Chen, Chongqing Education College (China)
Li Xiao, Chongqing Education College (China)
Jianfu Li, Chongqing Education College (China)

Published in SPIE Proceedings Vol. 6794:
ICMIT 2007: Mechatronics, MEMS, and Smart Materials
Minoru Sasaki; Gisang Choi Sang; Zushu Li; Ryojun Ikeura; Hyungki Kim; Fangzheng Xue, Editor(s)

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