基于支持向量机的商业银行对中小信贷企业选择方法的研究

Research of Loan Enterprise Selection for Bank Based on Support Vector Machine

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.