Social Post-Evaluation of World Bank Projects in Yanhe Basin Based on Ridge Regression and Support Vector Machines

The multicollinearity exists in the interpretive variable of regression model , it often brings inconvenience to social post-evaluation. The ridge regression has advantages than LS method. The support vector machines (SVM) is a novel machine learning tool in data mining. It is based on the structural risk minimization (SRM) principle, which has been shown to be more superior than the traditional empirical risk minimization (ERM). In this paper, we combined ridge regression and support vector machines to the World Bank projects in Yanhe Basin. Theoretical analysis and experimental results show that the combination is effective.