支持向量机(SVM)是一种新的通用学习机器，它从结构风险最小化的角度，分析了学习过程的一致性、收敛速度等。
Support vector machine (SVM) is a new general learning machine, which analyzes the consistency of learning and speed of convergence from structure 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.
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 (SVM) based on the structural risk minimization of statistical learning theory is a method of machine learning for small sample set.
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.
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.
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 operates on a principle, called structural risk minimization, which aims to minimize the upper bound on the expected generalization error.
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.
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.
应用推荐