Support Vector Machines Approach to Credit Assessment

机译：支持向量机的信用评估方法

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Credit assessment has attracted lots of researchers in financial and banking industry. Recent studies have shown that Artificial Intelligence (AI) methods are competitive to statistical methods for credit assessment. This article applies support vector machines (SVM), a relatively new machine learning technique, to the credit assessment problem for better explanatory power. The structure of SVM has many computation advantages, such as special direction at a finite sample and irrelevance between the complexity of algorithm and the sample dimension. A real credit card data experiment shows that SVM method has outstanding assessment ability. Compared with the methods that are currently used by a major Chinese bank, the SVM method has a great potential superiority in predicting accuracy.

机译：信用评估吸引了金融和银行业的许多研究人员。最近的研究表明，人工智能（AI）方法与信用评估的统计方法相比具有竞争优势。本文将支持向量机（SVM）（一种相对较新的机器学习技术）应用于信用评估问题，以获得更好的解释力。支持向量机的结构具有许多计算优势，例如有限样本的特殊方向以及算法复杂度和样本维数之间不相关。真实的信用卡数据实验表明，支持向量机方法具有出色的评估能力。与大型中国银行目前使用的方法相比，支持向量机方法在预测准确性方面具有很大的潜在优势。

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来源
《International Conference on Computational Science pt.4; 20040606-20040609; Krakow; PL》|2004年|P.892-899|共8页
会议地点 Krakow(PL);Krakow(PL)
作者
Jianping Li; Jingli Liu; Weixuan Xu; Yong Shi;
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作者单位

University of Science Technology of China, Hefei, 230026, P.R.China;

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会议组织
原文格式 PDF
正文语种 eng
中图分类理论、方法;
关键词
credit assessment; classification; support vector machines;

机译：信用评估；分类；支持向量机;

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Support Vector Machines Approach to Credit Assessment

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