支持向量机是Vapnik等人提出的以结构风险最小化原理(Structural Risk Minimization Principle)的分类理论，根据有限的样本信息在模型的复杂性和学习能力之间寻求最佳折衷，以期获得最好的泛化能力。
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Because the structural risk minimization principle makes SVM exhibit good generalization.
结构风险最小化原则使其具有良好的学习推广性。
The SVM (support vector machines) is a classification technique based on the structural risk minimization principle.
是一种基于结构风险最小化原理的分类技术。
Secondly, on the basis of these bounds, the idea of the structural risk minimization principle based on birandom samples is presented.
以这些界为基础，给出基于双重随机样本的结构风险最小化原则。
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