Support vector machine (SVM) is a new learning machine based on the statistical learning theory.
支持向量机是一种基于统计学习理论的新型机器学习方法。
The support vector machine (SVM) is a new learning machine based on the statistical learning theory.
支持向量机是一种基于统计学习理论的新型学习机。
The support vector machine(SVM) is a new learning technique based on the statistical learning theory.
支持向量机（SVM）是根据统计理论提出的一种新的学习算法。
Support vector machines (SVM) based on the statistical learning theory is a new machine learning tool.
基于统计学习理论的支持向量机是一种新型机器学习工具。
VC dimension plays a central role in the Statistical Learning Theory especially for classification problems.
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。
Support vector machine (SVM) is a new generation machine learning technique based on the statistical learning theory.
支持向量机(SVM)是基于统计学习理论的新一代机器学习技术。
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.
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。
As one algorithm of the machine learning based on the statistical learning theory, Support Vector machine (SVM) is specifically to the small samples learning case.
支持向量机是一种基于统计学习理论的机器学习算法，能够较好地解决小样本的学习问题。
Secondly, the text studies the Statistical Learning Theory(STL) and Support Vector Machine(SVM)theory seriously, discusses multi-category classification algorithms of SVM.
其次，认真研究了统计学习理论的主要内容和SVM算法的基本原理，并且就SVM的多种多类别分类算法分别加以讨论。
The Support Vector machine (SVM) is a new machine learning method based on the statistical learning theory and it is very useful to solve nonlinear problems of short time series.
支持向量机(SVM)方法是基于统计学理论的一种新的机器学习方法，对解决小样本条件下的非线性问题非常有效。
With an overview on the statistical learning theory and the related optimization theory, we expound the basic knowledge of SVR model and point out the advantages and disadvantages of SVM.
在对统计学习理论以及相关的优化理论进行回顾的基础上，从四个方面详细描述了SVR模型的基础知识，并指出了SVM的优缺点。
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.
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。
Compared with statistical theory, statistical learning theory focuses on the machine learning of small sample size and can trade off between the complexity of models and generalization performance.
与传统统计学相比，统计学习理论是一种专门研究小样本情况下机器学习规律的理论。
The basic statistical learning theory (SLT) and its corresponding algorithms, support vector machines (SVMs), are surveyed, and especially, its latest research results are summarized and studied.
为了系统地归纳统计学习理论与支持向量机的基本思想和算法，总结目前该领域的最新研究成果。
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 (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。
In this thesis, the current groundwater quality prediction methods were systematically summarized and the essential issues and main contents of statistical learning theory are elaborated.
本文对目前地下水环境质量预测的研究方法进行了系统总结，详细地阐述了统计学习理论研究的基本问题及主要内容。
Based on statistical learning theory (SLT), the relevant problems of solving the machinery intelligent diagnosis and condition prediction are thoroughly researched in this project by means of SVM.
本项目以统计学习理论为基础，深入研究了应用支持向量机方法解决机械智能诊断和状态预测的相关问题。
In this paper, statistical learning theory and support vector machine method are introduced in eor potentiality prediction for the first time.
本文首次将统计学习理论及支持向量机方法引入提高采收率方法的潜力预测中。
Support vector machine is a kind of machine learning algorithm based on statistical learning theory which mainly researches the learning of limited number of samples.
支持向量机是一种基于统计学习理论的机器学习方法，该理论主要研究在有限样本下的学习问题。
Currently, the support vector machine (SVM) which based on statistical learning theory is a research hotspot.
基于统计学习理论的支持向量机是当前机器学习领域的一个研究热点。
Support Vector machine is one of the hot points in machine learning research, it's theoretical basis is Statistical learning Theory.
支持向量机是机器学习领域的研究热点之一，其理论基础是统计学习理论。
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.
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。
Finally the key theorem of statistical learning theory based on random rough samples is proved, and the bounds on the rate of uniform convergence of learning process are discussed.
最后证明基于双重随机样本的统计学习理论的关键定理并讨论学习过程一致收敛速度的界。
SVM is a novel powerful machine learning method developed in the framework of Statistical Learning Theory (SLT).
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。
Statistical Learning Theory is based on a solid theoretical foundation. It provides an unified framework for solving the small sample learning problem.
统计学习理论具有坚实的理论基础，为解决小样本学习问题提供了统一的框架。
Support vector machine (SVM) is the best general machine learning theory developed from statistical learning theory, and suit to do prediction from small samples by learning.
本文采用统计学习理论，建立了基于最小二乘支持向量机的永磁操动机构动作时间预测模型。
Support Vector Machine is a new method based on the idea of VC dimension and Statistical Learning Theory in data mining.
支持向量机是基于VC维和统计学习理论理念的数据挖掘中的一种新方法。
The course focuses on the problem of supervised learning within the framework of Statistical Learning Theory.
这个课程的重点将放在与统计学习理论架构中有关的监督式学习问题。
Support Vector Machine (SVM) is used as the implementation basis, which is a tool of Statistical Learning Theory (SLT).
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。
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