是一种基于结构风险最小化原理的分类技术。
The SVM (support vector machines) is a classification technique based on the structural 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。
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.
支持向量机(SVM)是一种基于结构风险最小化原理，具有很好推广性能的学习算法。
The support vector machine (SVM) is an algorithm based on structure risk minimizing principle and high generalization ability.
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。
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.
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 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.
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 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 is a learning technique based on the structural risk minimization principle as well as a new regression method with good generalization ability.
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