Support vector machine (SVM) is a new general learning machine, which analyzes the consistency of learning and speed of convergence from structure risk minimization principle.
支持向量机(SVM)是一种新的通用学习机器，它从结构风险最小化的角度，分析了学习过程的一致性、收敛速度等。
Support vector machine (SVM) is a new machine learning technique.
支持向量机(SVM)是一种新型的机器学习方法。
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。
The prediction method of network delays based on support vector machine (SVM) was put forward.
进而提出了基于支持向量机(SVM)的网络延时预测方法。
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。
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。
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, summarize the application of the machine learning methods in the bioinformatics and expatiate on the rationale of the Support Vector Machine (SVM).
总结归纳了机器学习方法在目前生物信息学的应用，并对支撑向量机（SVM）算法的基本原理做了阐述；
According to the features of color texture image of maize disease, a method of recognizing disease by using support vector machine (SVM) and chromaticity moments is introduced.
针对玉米病害叶片彩色纹理图像的特点，提出一种将支持向量机和色度矩分析应用于玉米病害识别的方法。
According to the features of color texture image of plant disease, recognition of plant disease using support vector machine (SVM) and chromaticity moments was introduced.
针对植物病害彩色纹理图像的特点，提出将支持向量机和色度矩分析方法相结合应用于植物病害识别中。
Presents an efficient method of fabric defect classification based on cluster analysis and support vector machine (SVM).
提出一种基于聚类分析和支持向量机(SVM)的布匹瑕疵分类方法。
A novel evaluation method of customer satisfaction degree (CSD) in logistics based on support vector machine (SVM) was presented.
提出了一种新的基于支持向量机(SVM)的物流服务顾客满意度(CSD)评价方法。
The selection of the kernel function parameter and error penalty factor affected the precision of the support vector machine (SVM) significantly.
核函数参数和误差惩罚因子的选择对支持向量机模型（SVM）的精度有较大影响。
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 (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 an algorithm based on structure risk minimizing principle and high generalization ability.
支持向量机(SVM)是一种基于结构风险最小化原理，具有很好推广性能的学习算法。
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。
Motivated by the virtual reference feedback tuning (VRFT) method, we propose a new direct nonlinear controller design method using virtual reference (VR) and support vector machine (SVM).
本文基于虚拟目标值反馈调整(VRFT)方法的思想，利用支持向量机(SVM)，给出一种非线性控制器直接设计方法。
In order to increase accuracy in gender classification, an iterative learning approach combining support vector machine (SVM) and active appearance model (AAM) was proposed.
为了提高性别检测的精度，提出了一种支持向量机(SVM)与主动外观模型(aam)相结合的迭代学习算法。
Furthermore, combined with the nearest distance classifier, the support vector machine (SVM) is used for classification.
然后再以支持向量机(SVM)和最近邻分类法相结合组成分类器进行分类。
This paper presents the application of a recently-developed pattern classifier called support vector machine(SVM) in expressway incident detection.
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。
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。
A network traffic anomaly detection mechanism is presented based on support vector machine (SVM).
提出了一种基于支持向量机的网络流量异常检测方法。
A text detection method was presented based on support vector machine(SVM) using the statistics features characterizing character strokes.
提出一种利用笔画线条的统计特征基于支持向量机进行图像中叠加文字检测的方法。
Support vector machine (SVM) is applied to recognize two separable classes.
支持向量机用于两类问题的识别研究。
To solve the problem that support vector machine(SVM) can only classify the small samples set, a new algorithm which applied SVM to density clustering is proposed.
为了解决支持向量机的分类仅应用于较小样本集的问题，提出了一种密度聚类与支持向量机相结合的分类算法。
The classification mechanism of support vector machine (SVM) was analyzed in detail. The one dimensional image of radar target was recognized by SVM.
研究了支撑矢量机的分类机理，并利用支撑矢量机对雷达目标一维像进行了识别。
A new method of fault classification for mechanical system by means of support vector machine (SVM) is proposed and a multi-class SVM classifier based on binary classification was developed.
提出了一种利用支持向量机(SVM)对机械系统故障进行分类的新方法；以二值分类为基础，开发了基于支持向量机的多值分类器。
A new method of fault classification for mechanical system by means of support vector machine (SVM) is proposed and a multi-class SVM classifier based on binary classification was developed.
提出了一种利用支持向量机(SVM)对机械系统故障进行分类的新方法；以二值分类为基础，开发了基于支持向量机的多值分类器。
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