一种新隶属度函数的模糊支持向量机  

Fuzzy Support Vector Machine with New Membership Function

作　　者：杜喆[1] 刘三阳[1] 齐小刚[1] 

机构地区：[1]西安电子科技大学应用数学系,西安710071

出　　处：《系统仿真学报》2009年第7期1901-1903,共3页Journal of System Simulation

基　　金：国家自然科学基金资助项目(60574075)

摘　　要：传统的模糊支持向量机隶属度函数都是基于样本与类中心距离进行设计,这对非球形分布数据很不合理。使用类内超平面代替类中心,提出基于样本到超平面距离的新隶属度函数设计方法。该方法克服传统方法的不足,降低隶属度函数对样本集几何形状的依赖,提高模糊支持向量机的泛化能力。数值实验表明,与支持向量机和三种不同隶属度函数的模糊支持向量机相比,新隶属度函数的模糊支持向量机达到最好的分类效果,而且新隶属度方法的简单易行,计算时间少。Traditional membership functions in fuzzy SVM （FSVM） were designed based on the distance between a sample and its cluster center, which are irrational for dataset with non-spherical-shape distribution. A new membership function was proposed based on the distance between a sample and a hyperplane within the class. It overcomes disadvantages of traditional designing methods and improves the generalization ability of FSVM, while reducing the dependence of membership function on the geometric shape of sample data. Numerical experiments show that, compared with the traditional SVM and three FSVM with different membership functions, FSVM with new membership function has better classification accuracy. The New method is simple and its computation time is less.

关 键 词：支持向量机 模糊支持向量机 隶属度函数 分类 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]