关键词: 双支持向量机, 支持向量机, 逆矩阵, 核技巧, 模糊隶属度, 分类

Abstract: Twin support vector machine is a novel nonparallel binary classification, and its processing speed is much faster than the traditional support vector machine, But the twin support vector machine need to compute the large complex inverse matrices before training. In the nonlinear case, the kernel trick can not be applied directly to the dual optimization problems as traditional SVM, and the twin support vector machine do not consider the effects that different input samples have different effects on the optimal separating hyperplanes. In view of this, this paper proposes a fuzzy simple twin support vector machine. The fuzzy simple support vector machine by dual formulation and Lagrangian improvements, a large number of inverse matrix calculation is omitted, and kernel trick can be directly applied to the non-linear classification; The hybrid fuzzy membership function is not only affected by the distance between each sample point and center, but also affected by neighborhood density of the sample points. Experiments show that, compared with the support vector machines, standard two twin support vector machine, twin bounded support vector machine and fuzzy twin support vector machine, with the hybrid fuzzy membership function of the fuzzy twin support vector machine classification algorithm not only the classification time is short, simple calculation and high accuracy of classification.

Key words: twin support vector machine, support vector machine, inverse matrices, kernel trick, fuzzy membership, classification