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中文摘要：
分析基于样本与类中心距离设计模糊支持向量机隶属度函数的缺点,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隶属度函数设计方法。该方法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。最后数值实验表明,与传统的支持向量机和现有的3种不同隶属度函数的模糊支持向量机相比,新隶属度函数可达到最好的分类效果而且速度快。
英文摘要：
On analyzing disadvantages of membership functions available based on the distance between a sample and its cluster center in fuzzy SVM. A new membership function is presented, based on the distance from a hyperplane within the class. The generalization ability of FSVM is improved, while the dependence on the geometric shape of sample data is reduced. Numerical experiments show that, compared with the traditional SVM and three fuzzy SVM with different membership functions, the new membership function has better classification accuracy and higher speed.
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