在统计学习理论中，尤其对于分类问题，VC维扮演着中心作用。
VC dimension plays a central role in the Statistical Learning Theory especially for classification problems.
支持向量机是在统计学习理论基础上开发出来的一种新的、非常有效的机器学习方法。
SVM is a novel powerful machine learning method developed in the framework of Statistical Learning Theory (SLT).
支持向量机（SVM）是建立在统计学习理论基础上的一种小样本机器学习方法，用于解决二分类问题。
Support Vector Machines(SVM) are developed from the theory of limited samples Statistical Learning Theory (SLT) by Vapnik et al. , which are originally designed for binary classification.
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.
支撑矢量机是根据统计学习理论提出的一种新的学习方法，即使用核函数在高维空间里进行有效的计算。
The support vector machine is a novel type of learning technique, based on statistical learning theory, which USES Mercer kernels for efficiently performing computations in high dimensional Spaces.
在探索手写字符识别的方法上采用了统计学习理论，利用支撑向量机SVM作为基本的识别工具。
Support Vector Machine (SVM) is used as the implementation basis, which is a tool of Statistical Learning Theory (SLT).
本文在经典统计学习理论的基础上，讨论了可能性空间上学习过程一致收敛速度的界。
In this paper, the bounds on the rate of uniform convergence of the learning processes on possibility space are discussed based on the classic Statistical learning Theory.
在分类器的设计上，重点讨论了最近邻分类器和基于统计学习理论的支持向量机(SVM)。
We emphases discussed the nearest neighbor classifier and support vector machine (SVM) based on the statistical study theory.
文章从统计学习理论入手，在讲述SVM一般原理的基础上，分析比较不同种的支持向量机的性能。
Studying from the statistical theory, based on the general principle of SVMs, this paper analyzes and compares the capability of the different kinds of SVMs.
接着对统计学习理论进行了介绍，深入探讨了建立在该理论基础上的SVM算法。
Secondly, the basic knowledge of the statistical learning theory has been introduced and the SVM based on the theory has been gone deep into discussed.
该方法以统计学习理论为基础，通过和BP神经网络进行比较的实验，证明其在交通量预测中的有效性。
SVM algorithm is based on statistical theory. Analysis of the experimental results proved that the algorithm of could achieve much effective than that of BP neutral network.
该方法以统计学习理论为基础，通过和BP神经网络进行比较的实验，证明其在交通量预测中的有效性。
SVM algorithm is based on statistical theory. Analysis of the experimental results proved that the algorithm of could achieve much effective than that of BP neutral network.
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