Fuzzy support vector machine algorithm with dual membership

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Fuzzy support vector machine algorithm with dual membership

Received:2007-05-08 Revised:2007-06-27 Online:2007-11-01 Published:2007-11-01
Contact: Ying Huang

双隶属度模糊支持向量机算法

黄颖李伟刘发升

江西理工大学

通讯作者: 黄颖

Abstract

Abstract: 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.

Key words: support vector machine, fuzzy support vector machine, membership, dual membership

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。

关键词: 支持向量机, 模糊支持向量机, 隶属度, 双隶属度

Ying Huang . Fuzzy support vector machine algorithm with dual membership[J]. Journal of Computer Applications.

黄颖李伟刘发升. 双隶属度模糊支持向量机算法[J]. 计算机应用.

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