Research of risk assessment and prediction based on support vector machine

Dakai Li; Yu Li; Zhang Qi-Wen

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Research of risk assessment and prediction based on support vector machine

机译：基于支持向量机的风险评估与预测研究

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Building a suitable credit risk evaluation model is very important, that is because loan business is one of the most important assets of commercial Banks. In this paper, we introduce a kind of learning algorithm which contains small sample learning and construct a new method which is called support vector machine (SVM). SVM is developed based on a new theory that is popular used in the field of intelligent learning system in recent years. The credit risk assessment model of commercial bank is established finally. By multiple discriminate analyses and the comparison of neural network model, confirms the validity and superiority of the method when be used in risk assessment.

机译：建立合适的信用风险评估模型非常重要，这是因为贷款业务是商业银行最重要的资产之一。在本文中，我们介绍了一种含有小样本学习的学习算法，并构建一种称为支持向量机（SVM）的新方法。 SVM是基于近年来智能学习系统领域流行的新理论开发的。最终建立商业银行的信用风险评估模型。通过多次判断分析和神经网络模型的比较，确认在风险评估中使用时方法的有效性和优越性。

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《BioTechnology: An Indian Journal》 |2014年第13期|共7页
作者
Dakai Li; Yu Li; Zhang Qi-Wen;
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Neural network modelIntelligent learning systemNonlinear technologyNon-normaldistributionMachine learning rule;

机译：神经网络ModelIndIntelligent学习SystemNonlinear TechnologyNon-IrmanyDistributionMachine学习规则;

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Research of risk assessment and prediction based on support vector machine

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