目前，支持向量机在模式识别、函数逼近、数据挖掘和文本自动分类中均有很好的应用。
Recently, Support Vector Machine is well applied in pattern recognition, function approximate, data mining and text auto categorization.
支撑矢量机是一种普适的算法，已经广泛地用于模式识别、回归估计、函数逼近、密度估计等方面。
SVM is a kind of general learning algorithms, which has been widely used in pattern recognition, regression estimation, function approximation, density estimation, etc.
本文讨论了一种采煤机切割状态的模式识别方法。
This paper discusses a new method of recognizing the shearer's cutting status based on pattern recognition.
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。
Support vector machine (SVM) is an important learning method of statistical learning theory, and is also a powerful tool for pattern recognition problems.
支持向量机作为数据挖掘的一项新技术，应用于模式识别和处理回归问题等诸多领域。
As new technology of data mining, support vector machines (SVM) have been successfully applied in pattern recognition and regression problem, et al.
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 an important learning method of statistical learning theory, and is also a powerful tool for pattern recognition.
文章采用一种新的模式识别技术——支持向量机（SVM），来进行高速公路的事件检测。
This paper presents the application of a recently-developed pattern classifier called support vector machine(SVM) in expressway incident detection.
针对电梯群控调度中的交通流模式识别问题，提出了一种基于多值分类支持向量机的电梯交通流模式识别方法。
Aiming at the pattern recognition of traffic flows in elevator group control systems, a method based on the multi-value classification SVM (Support Vector Machine) is put forward.
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。
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.
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 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.
支持向量机是一种新型的模式识别技术。
Support vector machine (SVM) is a novel pattern recognition technique.
支持向量机(SVM)是基于统计学习理论的一种智能学习方法，可以用来解决样本空间的高度非线性的模式识别等问题。
Support Vector Machine (SVM) is an intellectual learning method based on the statistics theory. The SVM can solve problems of complicated nonlinear pattern recognition of spatial samples.
结果表明，基于支持向量机的交通流模式识别方法能够较准确地辨识出各种交通流模式。
The results demonstrate that, by using the proposed method, different traffic flow modes can be recognized accurately.
归纳了支持向量机在诸如模式识别、函数逼近、时间序列预测、故障预测和识别、信息安全、电力系统以及电力电子领域中的应用。
SVM applications, such as pattern recognition, function approaching, time series prediction, fault prediction and recognition, information security, power system and power electronics, are described.
支持向量机分类器克服了当前常用的模式识别方法的缺点，有效提高了识别率。
Support vector machine classifier overcome the shortcoming of the present and commonly used pattern-recognition methods, and has improved the recognition rate effectively.
本文简述了化学模式识别方法的原理和特点，着重论述了线性学习机方法。
In this paper, the principals and characteristics of chemical pattern recognition method were described. Among this method, Linear Learning Machine was emphasized.
由于具有完备的理论基础和良好的性能，支持向量机已经成为模式识别的一个研究热点。
Due to its complete theoretical basis as well as excellent performance, SVM has become a hotspot in the area of pattern recognition.
摘要：在解决非线性、高维模式识别以及小样本等问题中，支持向量机表现出许多独有的优势。
Absrtact: SVM represented many unique advantages in many applications, such as solving the problem of nonlinear, high dimension pattern recognition and small sample problem.
介绍了支持向量机的基本思想，提出了一个基于支持向量机的粮虫模式识别系统。
The foundations of support vector machines are introduced. An evaluation model based on SVM is made, and the model is tested to obtain better results.
支持向量机是一种全新的机器学习方法，其出色的学习性能和泛化能力强等方面的优势，在模式识别领域中有其独到的优越性。
Support Vector machine is a new machine-learning method and has its unique advantages in pattern recognition because of outstanding learning performance and good capabilities in generalization.
针对数字信号调制模式识别问题，提出了运用高阶累积量和二叉树支持向量机(SVM)进行自动识别的算法。
Applying high order cumulants and support vector machines (SVM), an algorithm is proposed for automatic recognition of digital communication signals.
利用紫外光谱结合支持向量机(SVM)模式识别原理建立了短串联重复序列(STR)的分型方法。
An approach for genotyping of STR locus based on ultraviolet (UV) spectroscopy and support vector machine (SVM) was studied.
利用紫外光谱结合支持向量机(SVM)模式识别原理建立了短串联重复序列(STR)的分型方法。
An approach for genotyping of STR locus based on ultraviolet (UV) spectroscopy and support vector machine (SVM) was studied.
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