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中文摘要：
对于处理大规模问题的信用评分方法除要求达到一定的准确率之外，其速度、可解释性、简洁性等性能也非常重要。借鉴SMO的思想，首先提出一个基于三变量的改进的SVM学习算法，即将SVM问题分解为一系列含有三个变量的二次规划子问题，其优点是所求的相应松弛子问题都有解析解，使得该方法能够更加精确和快速地逼近最优解；其次将新算法应用于信用评分问题，在UCI机器学习库中的三个公共数据集上的数值试验表明了新方法的有效性：不仅节省了模型的计算代价，而且还提高了分类精度。
英文摘要：
A credit scoring method for a large problem not only achieves a certain its speed, interpretability, simplicity and other performance are also very important accuracy In this paper, a novel method called an improved SVM learning algorithm based on three-variable working set （ISVM-TV） is presented. This algorithm is derived by solving a series of the QP problems with only three points and the corresponding relaxation subproblems are solved analytically so that the proposed method approaches to the optimal solution more quickly. The proposed method is introduced to credit scoring and three datasets from UCI machine learning datasets are selected to demonstrate the method＇s competitive performance. Moreover, ISVM-TV shows a superior performance in saving the computational cost and improving classification accuracy.
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