模糊支持向量机是在支持向量机的基础上,通过考虑不同样本对支持向量机的作用而提出的一种分类方法,然而,该方法却忽视了给定样本集的结构信息.为此,将样本集中的结构信息引入到模糊支持向量机中,给出了一种结构型模糊支持向量机模型,利用拉格朗日求解方法,将其转换为一个具有约束条件的优化问题,通过求解该对偶问题,获得了结构型模糊支持向量机分类器.实验中选取标准数据集,验证了提出方法的有效性.

By considering the role of different samples on support vector machine, fuzzy support vector machine is presented based on support vector machine. However, it ignores the structural information of the given sample set. To this end, structural information of the given sample set is introduced into fuzzy support vector machine and obtained a structural fuzzy support vector machine model. It is converted to dual problem with quadratic programming using Lagrange method. Through solving this dual problem,the fuzzy support vector machine classifier is obtained. Experimental results in selected standard data sets demonstrate the effectiveness of the proposed method.