A regularized Fuzzy Support Vector Machine

ABSTRACT By considering the role of different samples, fuzzy support vector machine is presented based on support vector machine. However, it ignores the structural information of each class samples. In this paper, structural information of the samples is introduced to 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. To solve this dual problem obtains the fuzzy support vector machine classifier. Experimental results in selected datasets demonstrate the effectiveness of the proposed method.