摘要
支持向量机(SVM)是建立在统计学习理论的基础上的一种小样本机器学习方法,它是针对二分类问题而提出的,如何将二分类问题有效地推广至多分类问题是支持向量机研究的重要内容之一.介绍了现有提出的一些支持向量机多分类的方法,并比较其优缺点,在模糊支持向量机的基础上提出具有去噪声的模糊支持向量机的多分类的方法.
Support Vector Machines（SVM） are developed from the theory of limited samples Statistical Learning Theory （SLT） by Vapnik et al., which are originally designed for binary classification. How to effectively extend it for multi-class classification is still an on-going research issue. This paper introduces several typical existing multi-class classifiers and compares their advantage and disadvantages. And it also puts forward the algorithm of noise insensitive Fuzzy Support Vector Machines.
出处
《东莞理工学院学报》
2007年第5期65-69,共5页
Journal of Dongguan University of Technology
关键词
支持向量机
多类
模糊支持向量机
support vector machine
multi-class
fuzzy support vector machines
