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。
Support vector machine (SVM) based on the structural risk minimization of statistical learning theory is a method of machine learning for small sample set.
基于统计学习理论中结构风险最小化原则的支持向量机是易于小样本的机器学习方法。
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。
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。
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。
The SVM method is based on seeking on the Structural Risk Minimization by few learning samples supporting, and it has important feature such as good generalization and classification performance, etc.
支持向量机方法基于小学习样本条件下，通过寻求结构风险最小，以期获得良好的分类效果和泛化能力。
SVM can solve small sample problems and has good generalization ability using the principles of structural risk minimization.
支持向量机基于结构风险最小化原则，解决了小样本数据分类和泛化问题。
They USES Structural Risk Minimization and the kernel trick to solve the learning problems.
它使用结构风险最小化原则，运用核技巧，较好地解决了学习问题。
The main advantage of SVM is that it can serve better in the processing of small-sample learning problems by the replacement of Experiential Risk Minimization by Structural Risk Minimization.
由于使用结构风险最小化原则代替经验风险最小化原则，使它能较好地处理小样本情况下的学习问题。
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 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。
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 based on structural risk minimization can effectively solve the over study problem and has the good extension and the better classified accuracy.
它基于结构风险最小化原理，能有效地解决过学习问题，具有良好的推广性和较好的分类精确性。
It based on structural risk minimization can effectively solve the over study problem and the good extension and better classified accuracy.
它基于结构风险最小化原理，能有效地解决过学习问题，具有良好的推广性和较好的分类精确性。
By collecting samples for SVM training, the optimal separating surface with the minimization structural risk is developed for real-time monitoring of the functioning of the state transformer fan.
通过采集风机样本进行SVM训练，拟合出具有最小结构风险的最优分类面，用于实时监测变压器风机的运转状态。
By collecting samples for SVM training, the optimal separating surface with the minimization structural risk is developed for real-time monitoring of the functioning of the state transformer fan.
通过采集风机样本进行SVM训练，拟合出具有最小结构风险的最优分类面，用于实时监测变压器风机的运转状态。
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