[1]刘三阳 杜喆.一种改进的模糊支持向量机算法[J].智能系统学报,2007,2(03):30-33.
 LIU San-yang,DU Zhe.An improved fuzzy support vector machine method[J].CAAI Transactions on Intelligent Systems,2007,2(03):30-33.
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一种改进的模糊支持向量机算法(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第2卷
期数:
2007年03期
页码:
30-33
栏目:
出版日期:
2007-06-25

文章信息/Info

Title:
An improved fuzzy support vector machine method
文章编号:
1673-4785(2007)03-0030-04
作者:
刘三阳 杜喆
西安电子科技大学理学院,陕西西安710071
Author(s):
LIU San-yangDU Zhe
School of Science, Xidian University, Xi’an 710071, China
关键词:
模糊支持向量机隶属度函数分类
Keywords:
fuzzy support vector machine membership function classification
分类号:
TP181
文献标志码:
A
摘要:
模糊隶属度函数设计是模糊支持向量机中的关键步骤.Lin & Wang提出的基于类中心距离的模糊隶属度设计方法,不能从样本集中有效区分噪声或野值点,而且可能降低支持向量的隶属度.针对上述不足,提出一种改进的隶属度函数设计方法.通过引入一个半径控制因子,充分利用样本间的信息,更加合理地设计样本的模糊隶属度.与基于类中心的隶属度方法相比,该方法在不增加时间复杂度的情况下,通过数值实验表明了方法的优势,大大提高了模糊支持向量机的分类精度.
Abstract:
A design that improves the classifying ability of an SVM by improving the assignment of fuzzy membership is an important step in solving the fuzzy SVM problem. In this paper, a radius controlling factor is introduced to assign sample membership more accurately. This technique can distinguish noise and outliers and increase accuracy of membership in support vectors, compensating for the disadvantages of assigning fuzzy membership based on the distance between a sample and its cluster center proposed by Lin and Wang. Experimental results verify the effectiveness of this method .

参考文献/References:

[1] VAPNIK V. The nature of statistical learning theory[M]. New York: Spring er, 1995.
[2]CRISTIANINI N, TAYLOR S J. An introduction to support vector machines [M ]. Cambridge: Cambridge University Press, 2000.
[3]LIN C F, WANG S D. Fuzzy support vector machines[J]. IEEE Trans Neural Networks, 2002,13(2):464-471.
[4]INOUE T, ABE S.Fuzzy support vector machines for pattern classification[A ]. Proceedings of International Joint Conference on Neural Networks[C].Washington , D.C.,2001.
[5]HUANG H P,LIU Y H..Fuzzy support vector machines for pattern recognition a nd data mining[J]. International Journal of Fuzzy Systems, 2002,4(3):826-835. 
[6]ZHANG X G. Using class-center vectors to build support vector machines [A]. Proc IEEE NNSP’99[C].USA,1999.
[7]安金龙,王正欧,马振平.基于密度法的模糊支持向量机[J].天津大学学报,2004,37(6): 544-548.
 AN Jinlong, WANG Zheng’ou, MA Zhenping. Fuzzy support vector machine based o n densi ty[J]. Journal of Tianjin University, 2004, 37(6):544-548.
[8]边肇祺,张学工.模式识别(第2版)[M].北京:清华大学出版社,2000.

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备注/Memo

备注/Memo:
收稿日期:2006-10-08.
基金项目:国家自然科学基金资助项目(60574075).
 作者简介:
刘三阳,男,1959年生,博士,教授,博士生导师,主要研究方向为最优化理论与方法、网络算法及其在通信网中的应用.先后主持近20个科研项目,发表论文360多篇.
杜  喆  ,男, 1982年生,博士研究生, 主要研究方向为机器学习与最优化方法.E-mail:doog2005@tom.com.
更新日期/Last Update: 2009-05-07