摘要
传统的模糊支持向量机隶属度函数大多基于样本与类中心的距离设计,削弱了支持向量的作用。为此,结合2种隶属度函数,提出一种新的隶属度函数设计方法,将每一类样本划分为支持向量、非支持向量和孤立点。在赋予远离类中心的支持向量较大的隶属度同时,赋予远离类中心的非支持向量和孤立点较小的隶属度。实验结果表明,基于新隶属度函数的模糊支持向量机有更好的分类性能。
Membership functions of traditional Fuzzy Support Vector Machine （FSVM） are mostly designed based on the distance between the samples and the class centers, which decrease the effect of support vectors. This paper combines two membership functions and presents a new membership function to solve this problem. The new membership function divides the samples into three parts：support vectors,non-support vectors and outliers. It assigns large membership values to the support vectors which are far away from their class center. Small membership values are assigned to non-support vectors and outliers which are also far away from their class center. Experimental results show that the FSVM with the proposed membership function is more effective in classification.
出处
《计算机工程》
CAS
CSCD
北大核心
2016年第4期155-159,共5页
Computer Engineering
基金
国家自然科学基金资助项目(61373055)
高等学校博士学科点专项科研基金资助项目(20130093110009)
关键词
支持向量机
模糊支持向量机
支持向量
隶属度函数
分类
孤立点
Support Vector Machine （SVM）
Fuzzy Support Vector Machine （ FSVM ）
support vector
membership function
classification
isolated point
