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双隶属度模糊支持向量机算法 被引量：2

Fuzzy support vector machine algorithm with dual membership
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摘要 对现有的模糊支持向量机进行分析,提出一种改进的模糊支持向量机算法——双隶属度模糊支持向量机法(DM-FSVM)。在传统的模糊支持向量机模型中,每一个训练样本的隶属函数中只有一个隶属度,而DM-FSVM中每一个训练样本拥有两个隶属度。它既能保持传统模糊支持向量机的优点,又能充分利用有限样本,增加其分类推广能力。实验表明该算法较好地提高了分类精度。 Based on traditional fuzzy support vector machine （FSVM）, a new fuzzy support vector machine, dual membership fuzzy support vector machine （DM-FSVM）, was presented. There is only one membership in traditional support vector machine （SVM） model; however, there are two memberships in DM-FSVM. This method not only keeps the advantages of traditional FSVM, but also makes full use of limited data and improves the classification efficiencies. Experiments show that DM-FSVM improves the classification accuracy.
作者 黄颖 李伟 刘发升
机构地区 江西理工大学信息工程学院
出处 《计算机应用》 CSCD 北大核心 2007年第11期2821-2823,共3页 journal of Computer Applications
基金 江西省自然科学基金资助项目(0411046) 江西省高性能计算技术重点实验室资助基金项目(JXHC220052003) 江西省科技厅工业攻关项目(赣财教[2005]132号)
关键词 支持向量机 模糊支持向量机 隶属度 双隶属度 Support Vector Machine （SVM） Fuzzy Support Vector Machine （FSVM） membership dual membership
分类号 TP181 [自动化与计算机技术—控制理论与控制工程] TP311.131 [自动化与计算机技术—计算机软件与理论]
  • 相关文献

参考文献9

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共引文献22

同被引文献15

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  • 9LIN C F, WANG S D. Fuzzy support vector machines with automatic membership setting [J]. Studies in Fuzziness and Soft Computing, 2005,177: 233 -254.
  • 10LIN C F,WANG S D. Fuzzy support vector machines[ J]. IEEE Transactions on Neural Networks, 2002,13(2) : 464 -471.

引证文献2

二级引证文献6

计算机应用
2007年 第11期

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