摘要
引进了一种新的支持向量机的机器学习算法,解决了传统的机器学习算法在商业银行信贷企业选择方法上存在的局限性.以中小信贷企业为研究对象,运用支持向量机算法来解决分类问题和回归问题.通过某商业银行在中小信贷企业选择中的实际应用,并同神经网络训练得出的结果进行对比,证明这种支持向量机的机器学习算法,不仅具有较高的训练效率,而且有更高的精确度.
To overcome the limitation of the traditional machine learning algorithms in selecting loan enterprise for bank, a new machine-learning algorithm of Support Vector Ma- chine(SVM) is proposed. The algorithm differ from those traditional ones in as neural net- works. It could resolve the problem of loan enterprise for bank more efficiently. In order to show its superiority, a real training experiment based on the data from a bank is discussed in detail. Compared with the results derived from neural networks, the experiment results show that SVM not only improves the training efficiently, but also possesses higher accuracy.
作者
李苏
周小惠
宝哲
LI Su;ZHOU Xiao-hui;BAO Zhe(School of Economics, North Minzu University, Yinchuan 750021, China;Key Laboratory of Economic Management, State Ethnic Affairs Commission, Yinchuan 750021, China)
出处
《数学的实践与认识》
北大核心
2018年第11期299-305,共7页
Mathematics in Practice and Theory
基金
国家社会科学基金一般项目(14BMZ006)
北方民族大学科研项目(2016JJKY06)
北方民族大学重点科研项目(2015MYB02)
关键词
支持向量机
分类
统计学习理论
指标体系
support vector machine
classification
statistical learning theory
index system