A New Support Vector Classification Algorithm with Parametric-Margin Model

被引：3
作者：
Hao, Pei-Yi [1 ]
Tsai, Lung-Biao [1 ]
Lin, Min-Shiu [1 ]
机构：
[1] Natl Kaohsiung Univ Appl Sci, Dept Informat Management, Kaohsiung 807, Taiwan
来源：
2008 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-8 | 2008年
关键词：
D O I：
10.1109/IJCNN.2008.4633826
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, a new algorithm for Support Vector classification is described. It is shown how to use the parametric margin model with non-constant radius. This is useful in many cases, especially when the noise is heteroscedastic, that is, where it depends on x. Moreover, for a priori chosen v, the proposed new SV classification algorithm has advantage of using the parameter 0 <= nu < 1 on controlling the number of support vectors. To be more precise, nu is an upper bound on the fraction of margin errors and a lower bound of the fraction of support vectors. Hence, the selection of v is more intuitive. The algorithm is analyzed theoretically and experimentally.
引用
收藏
页码：420 / 425
页数：6
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