因此，对统计学习模型的复杂性给出评价与选择的准则，一直是一个核心问题。
It has long been recognized that the Structural Risk Minimization (SRM) principle based on the concept of VC-dimension provides an excellent means for complexity selection of a learning machine.
在对统计学习理论以及相关的优化理论进行回顾的基础上，从四个方面详细描述了SVR模型的基础知识，并指出了SVM的优缺点。
With an overview on the statistical learning theory and the related optimization theory, we expound the basic knowledge of SVR model and point out the advantages and disadvantages of SVM.
为此，结合统计学习理论的研究成果，建立了基于最小一乘准则的最优回归模型，并将其应用于商业银行的信贷风险评估中。
Thus, combined with research results of statistic learning theory, the optimal regress model based on least-absolute criteria, or LaOR model was proposed to solve the problem.
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