一种新的模糊补偿多类支持向量机

支持向量机是Vapnik等学者在统计学习理论的基础上提出的一种新的机器学习方法。针对支持向量机理论中的多类分类问题和对于噪音数据的敏感性，本文提出了一种模糊补偿多类支持向量机算法FC-SVM。该算法是在Weston等人提出的多类SVM分类器的直接构造方法中引入模糊补偿函数，针对每个输入数据对分类结果的两方面影响，将目标函数中的惩罚项不仅进行了模糊化，而且对于分类情况进行了加权补偿，并重构了优化问题及其约束条件，然后重构了Lagrange公式，给出了理论推导。在充分的数值实验基础上，将文中提出的方法应用于建设银行个人房贷的信用评估系统中，得到了较好的实验结果。

A Novel Fuzzy Compensation Multi-Class Support Vector Machine

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.