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。
It is a new statistical study method in which the traditional empirical risk minimization principle is replaced by structural risk minimization principle.
支持向量机是以统计学习理论为基础的，采用结构风险最小化原则代替传统经验风险最小化原则的新型统计学习方法。
Support vector machine is a learning technique based on the structural risk minimization principle as well as a new regression method with good generalization ability.
支持向量机是一种基于结构风险最小化原理的学习技术，也是一种新的具有很好泛化性能的回归方法。
The difference between them is that the former is based on the structural risk minimization principle and the latter is based on the experiential risk minimization principle.
不同的是，前者是基于结构风险最小化原理，后者基于经验风险最小化原理。
Support vector machine (SVM) is a novel and powerful learning method which is derived based on statistical learning theory (SLT) and the structural risk minimization principle.
建立在统计学习理论基础之上的支持向量机(SVM)，是一种基于结构风险最小的小样本机器学习方法。
Support vector machines is a new statistical learning method based on structural risk minimization principle, and it has integrated theory and valid learning generalization ability.
支持向量机是一种基于结构风险最小原则的新型机器学习方法，具有完备的理论依据和良好的学习泛化能力。
Based on the structural risk minimization principle, the latest data mining method, support vector machine (SVM) algorithm, in artificial intelligence field was introduced in this paper.
介绍了人工智能领域最新的基于结构风险最小化原理的数据挖掘算法——支持向量机算法。
Structural risk minimization induce principle is used to control the bound on the value of achieved risk by controlling experiential risk and belief bound at the same time.
结构风险最小化归纳原则通过控制经验风险和置信范围来控制实际风险的界。
It operates on a principle, called structural risk minimization, which aims to minimize the upper bound on the expected generalization error.
它基于结构风险最小化准则，目的是最小化泛化误差上界。
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.
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.
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。
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