Description: Support Vector Machines is a powerful methodology for solving problems in nonlinear classification and regression.
It is a matlab version.-Support Vector Machines is a powerful methamphetamine odology for solving problems in nonlinear CLAS sification and regression. It is a Matlab versi on. Platform: |
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Author:Mountain |
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Description: Support Vector Machines is a powerful methodology for solving problems in nonlinear classification and regression.
It is a matlab version.-Support Vector Machines is a powerful methamphetamine odology for solving problems in nonlinear CLAS sification and regression. It is a Matlab versi on. Platform: |
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Author:Mountain |
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Description: libsvm is a simple, easy-to-use, and efficient software for SVM
classification and regression. It solves C-SVM classification, nu-SVM
classification, one-class-SVM, epsilon-SVM regression, and nu-SVM
regression. It also provides an automatic model selection tool for
C-SVM classification. This document explains the use of libsvm.
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Author:baolij |
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Description: libsvm支持向量机，用于识别分类。本程序成功用于虹膜识别的研究。是嵌入matlab的C程序-libsvm support vector machine, used to identify classification. The success of this procedure for iris recognition research. Is embedded in the C program matlab Platform: |
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Author:张顺利 |
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Description: libsvm工具箱，用于分类的绝佳工具，也可用于非线性回归及预测，或拟合，其中文本分类是其长项，回归性能非常好-libsvm toolbox, an excellent tool for the classification can also be used for non-linear regression and forecasting, or fitting, of which text categorization is its long entry and return to a very good performance, Platform: |
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Author:陈墨 |
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Description: 基于GPU计算的SVM，VC++源码，包括详细文档说明文件。借用了GPU编程的优势，该代码据作者说比常规的libsvm等算法包的训练速度快13-73倍，预测速度快22-172倍。希望对大家有用-cuSVM is a software package for high-speed (Gaussian-kernelized) Support Vector Machine training and prediction that exploits the massively parallel processing power of Graphics Processors (GPUs). cuSVM is written in NVIDIA s CUDA C-language GPU programming environment, includes implementations of both classification and regression, and performs SVM training (prediction) at 13-73 (22-172) times the rate of state of the art CPU software. Moreover, cuSVM features a Matlab MEX wrapper so that users can access the GPU s power without having to do any "real" programming.
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Author:Sheng |
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Description: 1.GeometricContext文件是完成图片中几何方向目标分类。
参考文献《Automatic Photo Pop-up》Hoiem 2005
2 GrabCut文件是完成图像中目标交互式分割
参考文献《“GrabCut” — Interactive Foreground Extraction using Iterated Graph Cuts》
C. Rother 2004
3 HOG文件是自己编写的根据HOG特征检测行人的matlab代码
4 虹膜识别程序是下载的一个通用的虹膜识别程序，可以运行
5 GML_AdaBoost_Matlab_Toolbox是一个很好用的adaboost matlab工具箱
6 libsvm-mat-2.91-1 是用C编写的改进的SVM程序，代码质量很高，提供了matlab接口
7 SIFT_Matlab 是编写的利用sift特征进行的宽基线匹配，代码质量高
8 FLDfisher 是利用fisher 线性降维方法进行人脸识别-1.GeometricContext file is complete the picture in the geometric direction of target classification. References " Automatic Photo Pop-up" Hoiem 2005 2 GrabCut the target file is an interactive segmentation of image reference " " GrabCut " - Interactive Foreground Extraction using Iterated Graph Cuts" C. Rother 2004 3 HOG documents prepared under their own HOG Characteristics of pedestrian detection matlab code 4 iris recognition process is to download a general iris recognition program, you can run 5 GML_AdaBoost_Matlab_Toolbox is a good use of adaboost matlab toolbox 6 libsvm-mat-2.91-1 is written in C to improve the SVM procedures, code of high quality, provides a matlab interface to 7 SIFT_Matlab is prepared for the use of sift features a wide baseline matching, the code is the use of high quality 8 FLDfisher fisher linear dimension reduction method for face recognition Platform: |
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Author:张数 |
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Description: matlab编写的文本分类的程序，可以对已经分好词的文本进行分类，先自己导入数据，用libsvm中的svm进行分类和预测，特征用tfidf算法，还利用卡方检验进行了特征选择，可自行设定阈值。-matlab prepared text classification program, you can have a good word of text classification, classification and prediction using libsvm in svm, characterized by tfidf algorithm, also used the chi-square test was used for feature selection, you can set thresholds on their own. Platform: |
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Author:wang |
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Description: 支持向量机的源代码，可以实现分类和回归分析。-ctions on Intelligent Systems and Technology
ABSTRACT LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM. Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
we show formulations used in LIBSVM: C-support vector classifica-tion (C-SVC), ν-support vector classification (ν-SVC), distribution estimation (one-class SVM), -support vector regression (-SVR), and ν-support vector regression
(ν-SVR). We discuss the implementation of solving quadratic problems Platform: |
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Author:guolei |
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Description: SVM(Support Vector Machine)指的是支持向量机，是常见的一种判别方法。在机器学习领域，是一个有监督的学习模型，通常用来进行模式识别、分类以及回归分析。
Vapnik等人在多年研究统计学习理论基础上对线性分类器提出了另一种设计最佳准则。其原理也从线性可分说起，然后扩展到线性不可分的情况。甚至扩展到使用非线性函数中去，这种分类器被称为支持向量机(Support Vector Machine，简称SVM)。支持向量机的提出有很深的理论背景。
支持向量机方法是在后来提出的一种新方法。
SVM的主要思想可以概括为两点:
它是针对线性可分情况进行分析，对于线性不可分的情况，通过使用非线性映射算法将低维输入空间线性不可分的样本转化为高维特征空间使其线性可分，从而使得高维特征空间采用线性算法对样本的非线性特征进行线性分析成为可能。
它基于结构风险最小化理论之上在特征空间中构建最优超平面，使得学习器得到全局最优化，并且在整个样本空间的期望以某个概率满足一定上界。(442/5000
SVM(Support Vector Machine) refers to Support Vector Machine, which is a common discriminant method. In the field of machine learning, it is a supervised learning model, which is usually used for pattern recognition, classification and regression analysis.
Vapnik et al. proposed another design criterion for linear classifier on the basis of years of statistical learning theory. The principle is also derived from linear separability, and then extends to linear inseparability. Even extending to the use of nonlinear functions, this classifier is called Support Vector Machine (SVM). The support vector machine has a deep theoretical background.) Platform: |
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Author:飛上云端 |
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Search - libsvm for classification in maTLAB