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。
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 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。
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。
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 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。
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