It is confirmed that the support vector machine theory can predict the velocity of blasting vibration well.
证明支持向量机理论能较好地预测爆破振动合速度。
Chaos and support vector machine theory has opened up a new route to study complicated and changeable non-linear hydrology time series.
混沌和支持向量机理论为研究复杂多变的非线性水文时间序列开辟了新的途径。
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。
This paper mainly make research on classify methods based on statistical theory, support vector machine (SVM), and feature extraction method-wavelet transform, and using them in human face detection.
本文主要研究目前较为流行的基于统计学习理论的分类方法——支持向量机方法(SVM)，以及小波变换提取特征的方法，将其用于人脸检测。
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 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。
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。
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 (SVM) is a kind of learning machines constructed according to SRM principle on the basis of VC theory, which is much more powerful man the neural networks.
支撑矢量机（SVM）是在VC理论的基础上根据结构风险最小归纳原理建立的一种比神经网络更强有力的学习机。
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。
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 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。
By using rough set theory, this paper structures classification rules and processes the support vector machine feedback results with learning the train set.
利用粗糙集理论，通过对训练集的学习，构造分类规则，对支持向量机反馈后的结果再次进行处理。
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。
As an effect tool of pattern recognition and data processing, rough set theory (RST) and support vector machine (SVM) have become the focus of research in machine learning.
粗糙集理论(rst)与支持向量机(SVM)作为模式识别，数据处理的有效工具，已成为机器学习的研究热点。
Support vector machine is a kind of machine study algorithm based on statistic theory, it has special advantage in solving small sample, non-linear and high dimension mode recognition.
支持向量机是一种基于统计理论的机器学习算法，在解决小样本、非线性及高维模式识别中有独特的优势。
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) 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 is a new learning method based on VC theory, good generalization is required by minimizing the upper abound of expected risk.
支持向量机是一种基于VC理论的创造性学习方法，它能够使期望风险最小化，具有较强的推广能力。
Considering the complex influence factors for the failure of drill stem, an analytic model for failure of drill stem based on support vector machine and cluster analysis theory was established.
针对钻具失效影响因素比较复杂的问题，建立了基于支持向量机和聚类分析理论的钻具失效原因分析模型。
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。
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。
Support Vector machine is a kind of new machine studying method, which is based on Statistical Learning Theory.
支持向量机是基于统计学习理论的一种新的机器学习方法。
Support Vector Machine(SVM) is a machine learning method based on Statistical Learning Theory. It can solve a series of issues of Neural Networks.
支持向量机是一种基于统计学习理论的机器学习方法，它解决了神经网络中存在的一系列问题。
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。
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。
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。
Similarly, based on rough set theory to feature-set reduction, in the optimal decision based on the use of the property least squares support vector machine classifier to identify the flow pattern.
同样，基于粗糙集理论对特征集进行约简，在最优决策属性的基础上使用最小二乘支持向量机分类器对流型进行识别。
We emphases discussed the nearest neighbor classifier and support vector machine (SVM) based on the statistical study 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)。
Support vector machine (SVM) is an important learning method of statistical learning theory, and is also a powerful tool for pattern recognition problems.
支持向量机是统计学习理论的一个重要的学习方法，也是解抉模式识别问题的一个有力的工具。
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