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Support Vector Machine Classifier(SVM)
基于支持向量基的图像分类方法，程序变得很好，国外网站上下的，是一个完整的工程-Support Vector Machine Classifier (SVM) based on support vector-based image classification methods, procedures have become very good, abroad site from top to bottom, is a
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Abstract
We present a component-based, trainable system for detecting
frontal and near-frontal views of faces in still gray
images. The system consists of a two-level hierarchy of Support
Vector Machine (SVM) classifiers. On the first level,
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选用支持向量机作为区分文本与非文本的分类器，支持向量机是在统计学习理论基础上发展起来的新一代学习算法，它在解决小样本、非线性及高维模式识别问题中表现出许多特有的优势。-Use support vector machine as the distinction between text and non-text classifier, support vector machine is in statistical learning theory developed on the basis of
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模式识别中的线性分类器的设计，包括感知机，最小二乘法和支撑矢量机的算法的MATLAB代码。-Pattern Recognition linear classifier design, including perception, least squares and support vector machine algorithm MATLAB code.
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支持向量机(svM)是一种新的机器学习技术。本文采用一对一方法构建多分类SVM
分类器。利用常用的灰度共生矩阵方法提取图像纹理特征，组成特征向量，输入构建好的SVM
多分类器中进行分类。对从Brodatz纹理库中选取的4张纹理图像进行了分类实验，取得较好的
分类结果-Support vector machine (svM) is a new machine learning techniques. In this paper, one way to build a multi-cla
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的研究彩色数字图像的计算机分类识别方法并应用于古瓷片的自动分类。方法提出
了一种色彩纹理特征的提取模型，采用该模型，利用IGabor滤波器提取数字图像的色彩纹理特征，
并构造支持向量分类机(SVM)分类器组。结果实现了高准确率多类别图像的自动分类识别，并
成功应用于古瓷片的自动分类。结论色彩纹理特征提取方法将颜色与纹理进行融合，增强了数
字图像之间的特征区分能力。-Study color digital image classification and recognition m
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本文提出了一种基于特征融合的纹理图像分类方法，它结合了纹理图像的特
点和框架小波变换方法，处理过程中充分考虑了图像各尺度间的依存关系以及不
同频带中所包含的图像纹理信息，利用支撑矢量机作为分类器，对标准纹理库中
的图像进行了仿真实验。
-This paper presents a texture feature fusion based image classification method, which combines the special texture
Point
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Gabor小波提取特征源码，支持向量量机作分类器，能用于掌纹，人脸，指纹识别。-Gabor wavelet feature extraction, and then use the support vector machine as classifier, can be used to palmprint, face, fingerprint identification.
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利用HOG特征和支持向量机训练的到人体检测分类器，实现对人体的检测-HOG features and support vector machine training to the human detection classifier, the detection of the human body
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支持向量机分类器，可以用于手写分类，车牌字符分类等-Support vector machine classifier
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该程序实现机器学习中支持向量机（SVM）的功能，可以用来学习分类器。-The program realization of support vector machine learning (SVM) function, can be used to study the classifier.
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Gabor小波提取特征,然后用支持向量机作分类器，可以用于掌纹，人脸，指纹识别。-Gabor wavelet feature extraction, and then use the support vector machine as classifier, can be used for palmprint, face, fingerprint identification.
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图像场景分类的bow模型opencv源代码，采用k-means聚类构造单词，采用支持向量机的svm分类器。-Image scene classification bow model opencv source code, using k-means clustering structure of words, using support vector machine svm classifier.
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基于matlab的支持向量机多分类器train-Matlab-based support vector machine classifier train
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本程序融合机器学习，利用支持向量机训练车牌样本数据，然后用来识别测试数据，测试结果表明，该训练分类器正确率接近99 -This program fusion machine learning, support vector machine training license plate sample data were then used to identify the test data, test results show that the classifier is trained prop
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In machine learning, support vector machines (SVMs, also support vector networks[1]) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. Given a
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对提取的高维人脸特征进行降维。然后利用支持向量机分类器对其进行分类识别-High dimensional face feature extraction of dimensionality reduction. Then support vector machine classifier its classification
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AN MR BRAIN IMAGES CLASSIFIER VIA PRINCIPAL
COMPONENT ANALYSIS AND KERNEL SUPPORT
VECTOR MACHINE
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将这330个3780维的HOG特征当做测试样本，用支持向量机（SVM）分类器来判别出，这些窗口的HOG特征是否有行人，有行人的用矩形框标记起来。HOG行人特征及所对应的SVM分类器的参数，在opencv中已经训练好了，我们只需要得到HOG特征，然后调用SVM即可得到判别结果(The 330 Hera features of 3780 dimensions are used as test samples, and the support vector machine (SVM) classifi
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