Unsupervised clustering based reduced support vector machines

被引：0
|
作者：
Zheng, SF [1 ]
Lu, XF [1 ]
Zheng, NN [1 ]
Xu, WP [1 ]
机构：
[1] Xian Jiaotong Univ, Inst Artificial Intelligence & Robot, Xian 710049, Peoples R China
来源：
2003 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOL II, PROCEEDINGS: SPEECH II; INDUSTRY TECHNOLOGY TRACKS; DESIGN & IMPLEMENTATION OF SIGNAL PROCESSING SYSTEMS; NEURAL NETWORKS FOR SIGNAL PROCESSING | 2003年
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D O I：
暂无
中图分类号：
O42 [声学];
学科分类号：
070206 ; 082403 ;
摘要：
To overcome the vast computation of the standard support vector machines (SVMs), Lee and Mangasarian proposed reduced support vector machines (RSVM). But they select 'support vectors' randomly from the training set, and this will affect the test result. In this paper, we select some representative vectors as support vectors via a simple unsupervised clustering algorithm, and then apply the RSVM method on these vectors. The proposed method can get higher recognition accuracy with fewer support vectors compared to the original RSVM, with the advantage of reducing the running time significantly.
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页码：821 / 824
页数：4
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