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SVMstruct is a Support Vector Machine (SVM) algorithm for predicting multivariate or structured outputs. It performs supervised learning by approximating a mapping
h: X --> Y
using labeled training examples (x1,y1), ..., (xn,yn). Unlike regula
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Question classification using SVM.
Original paper by Dell Zhang, Wee Sun Lee SIGIR’03
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SVMstruct is a Support Vector Machine (SVM) algorithm for predicting multivariate or structured outputs. It performs supervised learning by approximating a mapping
h: X --> Y
using labeled training examples (x1,y1), ..., (xn,yn). Unlike reg
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SVM classification
This routine classify the training set with a support vector machine using quadratic programming algorithm (active constraints method)
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李荣陆老师做的文本分类器，用中科院分词系统做的，分类方法用的是SVM和K-Rong Lu teachers do text classification, word segmentation system with the Chinese Academy of Sciences to do, classification using a SVM and KNN
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用matlab的核函数对钓鱼岛实现分类（模式识别作业）-Kernel function using matlab realize the Diaoyu Islands classification (pattern recognition operations)
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一种用于数据分类的算法，与svm的作用类似-In statistics, logistic regression or logit regression is a type of probabilistic classification model[1] used for predicting the outcome of a categorical dependent variable (i.e., a class label) based on one or more predictor v
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This source code is for one time training of color images for the svm classification of the fruits (specifically used for apples). The SVM classifier used has been designed to work with multiple classes, whereas the code is utilizing only two class c
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This the source code for stomachcancer detection using active contour segmentation and SVM classification method-This is the source code for stomachcancer detection using active contour segmentation and SVM classification method
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This the source code blood cancer detection using color segmentation with SVM classification.-This is the source code blood cancer detection using color segmentation with SVM classification.
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this code product to classification of blood histology images.texture and color features used to patterns extraction and classified using SVM (support vector machine). texture features extracted with SRF gradient and LBP operator and to color feature
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利用hog特征以及svm实现两个类别图像的分类(Classification of two categories of images using hog features and SVM)
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运用支持向量机对所选择的目标进行分类处理，这类分类处理比较与其他方法较为实用(Using support vector machine to classify the selected target, this kind of classification is more practical than other methods.)
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matlab 开发，使用SVM方法实现印章和背景的自动分类，分类效果较好(Matlab development, using SVM method to realize automatic classification of seal and background, the effect of classification is better)
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脑机接口分类，特征提取用小波包熵，分类使用SVM分类器(Brain-computer interface classification, feature extraction using wavelet packet entropy, classification using SVM classifier)
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利用原算法adaboost弱学习器基于决策树桩的方法对样本数据进行多分类(Multi-classification of sample data based on decision tree stump using AdaBoost weak learner)
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