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利用SVM算法来进行中文文本的分类，如一句话里有各种词性的词语则可以进行分类处理-Using SVM algorithm for Chinese text categorization, such as a word in a variety of terms can be part of speech classification
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分别使用感知机，最小二乘，SVM进行样本分类的基础算法。-Perceptron，least squares,SVM based method were used for sample classification.
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实现三类的SVM分类，实现三类模式识别，对图像识别作用比较大，基于MATLAB7实现-Realizing the SVM classification, three kinds of pattern recognition, and image recognition based on bigger role, MATLAB7 realization
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实现SVM数据挖掘分类模型，主要构造了SVM分类器。-Data mining to achieve SVM classification model, the main structure of the SVM classifier.
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用MATLAB编写的svm源程序，可以实现支持向量机，用于特征分类或提取-MATLAB svm prepared by the source, can achieve a support vector machine for the feature classification or extract
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feature selection for svm classification
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libarary of svm for classification
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用matlab实现的svm源程序。这个程序可以进行svm分类。-Svm matlab implementation with source code. This procedure can be svm classification.
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使用支持向量机SVM神经网络分类数据的预测-the prediction of data classification by SVM neural network
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SVM进行分类的一个实例，是用于对酒的化学成分分类的-An example of SVM classification is used for classification of chemical composition of wine
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SVM CLASSIFICATION AND REGRESSION
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附带四种SVM工具箱的分类以及回归算法源代码-Four SVM classification and regression algorithm toolbox
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classification with svm
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台湾林智仁编写的支持向量机开源程序，可用于分类（C-SVC，nu-SVC，one-class SVM）和回归（epsilon-SVR，nu-SVR）。这是最新版本3.0。-Libsvm3.0 is a simple, easy-to-use, and efficient software for SVM
classification and regression. It solves C-SVM classification, nu-SVM
classification, one-cla
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svm classifier for gene classification
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SVM（支撑矢量机）用于分类，非常有效，只要函数运用恰当，参数设计准确，该方法在现在运用十分广泛-SVM (support vector machine) is used for classification, very effective, as long as the function of the use of appropriate and accurate design parameters, the method is widely used in the present
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SVM matlab 工具箱，可以进行SVM分类-SVM matlab toolbox, SVM classification can be
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四种支持向量机的四种SVM工具箱的分类与回归算法，包括全部工具箱函数。-Four of four SVM support vector machine classification and regression algorithm toolbox, including all the toolbox functions.
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PCA特征抽取与SVM多类分类在传感器故障诊断中的应用PCA feature extraction and SVM multi-class classification in the sensor fault diagnosis-PCA feature extraction and SVM multi-class classification in the sensor fault diagnosis
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svm分类的面确定，使用了最近邻的算法，归类以后得到一个分界面-svm classification of the surface determined using the nearest neighbor algorithm, obtained after classification of a sub-interface
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