模糊多类SVM模型

利用SVM处理多类分类问题,是当前的研究热点之一.本文提出了一种模糊多类支持向量机模型,即FMSVM.该方法是在Weston等人提出的多类SVM模型中引入模糊成员函数,针对每个输入数据对分类结果的不同影响,该模糊成员函数得到相应的值,由此得到不同的惩罚值.从而在构造分类超平面时,可以忽略那些对分类结果影响很小的数据.理论分析与数值实验都表明,该算法具有良好的鲁棒性.

Fuzzy Support Vector Machine for Multi-Class Classification

How to process multi-class problem with SVM is one of the present research focuses.We propose a fuzzy multi-class SVM model referred as FMSVM.It is constructed by introducing a fuzzy membership function to the penalty in the quadratic problem of Weston and Watkins,the membership function acquire different values for each input data according to their different affects on the classification results.Hence,we can ignore the data,which affect the classification result a little.Therefore different input points can make different contributions to the learning of the decision surface,i.e.,the optimal separating hyper-plane.Both theoretical analysis and digital experiment results show that the model proposed here works very well on benchmark data sets and also has the property of robustness.