Fuzzy Support Vector Machines Based on Convex Hulls

被引：1

作者：

Liu, Hongbing ^{[1
]}

Xiong, Shengwu ^{[1
]}

Chen, Qiong ^{[1
]}

机构：

[1] Wuhan Univ Technol, Sch Comp Sci & Technol, Wuhan 430070, Peoples R China

来源：

2008 IEEE INTERNATIONAL SYMPOSIUM ON KNOWLEDGE ACQUISITION AND MODELING WORKSHOP PROCEEDINGS, VOLS 1 AND 2 | 2008年

关键词：

support vector machines; fuzzy support vector machines; convex hulls; fast fuzzy;

D O I：

10.1109/KAMW.2008.4810642

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Fast Fuzzy Support Vector Machines (FFSVMs) based on the convex hulls are proposed in this paper. Firstly, the convex hull of each class data is generated by using the quick hull algorithm, and the data points lying inside the convex hull are not important to form FSVMs and then discarded. Secondly, the reduced training set consisting of the convex points is used to train the FFSVMs. Thirdly, the benchmark two-class problems and multi-class problems datasets are used to test the effectiveness and validness of FFSVMs. The experiment results indicate that FFSVMs not only reduce the training set but also achieve the same or better performance compared with the traditional FSVMs.

引用

页码：920 / 923

页数：4

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