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。
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。
Support Vector Machines algorithm is a simple and effective classification method based upon statistical learning theory.
支持向量机是基于统计学习理论框架下的一种简单、有效的分类方法。
Support Vector Machine (SVM) is an important learning method of statistical learning theory, and is also a powerful tool for pattern recognition.
支持向量机是统计学习理论的一个重要学习方法，也是解决模式识别问题的一个有力工具。
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。
This paper studies SVM and its theory basic-statistical learning theory.
文章系统地介绍了支持向量机和其理论基础——统计学习理论。
Support vector machines based on statistical learning theory is a typical example.
基于统计学习理论的支持向量机便是一个典型的例子。
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。
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。
Transductive inference based on support vector machine is a relatively new research region in statistical learning theory.
基于支持向量机的直推式学习是统计学习理论中一个较新的研究领域。
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。
This paper introduces statistical learning theory and support vector machine, proposes a new method, support vector machine technology, to simulate quasi-geoid.
介绍统计学习理论和支持向量机，提出利用支持向量机技术进行似大地水准面拟合。
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。
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 (SVM) is a brand-new machine learning technique based on statistical learning theory. It is an ideal facility for modeling of various nonlinear systems.
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，它可以被广泛地用于非线性系统建模。
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。
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。
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。
Support vector machines (SVM) are a kind of novel machine learning methods based on statistical learning theory, which has been developed to solve classification and regression problems.
支持向量机是一种基于统计学习理论的新颖的机器学习方法，该方法已广泛用于解决分类和回归问题。
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。
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。
Support vector machines (SVM) are a kind of novel machine learning methods, based on statistical learning theory, which have been developed for solving classification and regression problems.
支持向量机是一种基于统计学习理论的新颖的机器学习方法，该方法已广泛用于解决分类和回归问题。
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。
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。
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。
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。
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。
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