A Novel Fuzzy Compensation Multi-Class Support Vector Machine
一种新的模糊补偿多类支持向量机

Support vector machine (SVM), proposed by Vapnik based on statistical learning theory (SLT), is a novel machine learning method which has been applied to many application fields successfully. But there are two kinds of problems to be solved in such field, one is the multi-class classification problem, and the other is the sensitivity to the noisy data. In order to overcome these difficulties, a novel method of fuzzy compensation multi-class support vector machine is proposed in this paper, which is named as FC-SVM in the present paper. This method imports a fuzzy compensation function to the penalty in the straightly construction multi-class SVM classification problem proposed by Weston and Watkins. Aiming at the dual affects to classification results by each input data, this method has punish item be fuzzy, compensates weight to classification, reconstructs the optimization problem and its restrictions, reconstructs Langrage formula, and presents the theories deduction. This method is applied to the credit evaluating system of personal loan. The experiment presents this method is feasible.