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[Other resourcelibsvm-weight-2.81

Description: 一种基于局部密度比权重设置模型的加权支持向量回归模型来单步求解多分类问题:该方法先分别对类样本中每类样本利用局部密度比权重设置模型求出每个样本的权重隶属因子,然后运用加权lib支持向量回归算法对所有样本进行训练,获得回归分类器,希望对大家有用!
Platform: | Size: 41866 | Author: 郭大 | Hits:

[Other resourcelibsvm-mat-2.8-1

Description: 这是关于支撑向量机算法的matlab程序
Platform: | Size: 67430 | Author: 杨雨茜 | Hits:

[Other resourcelibsvm-mat-2.8-1

Description: 人工智能模式识别中基于支持向量机的分类算法在识别领域属于较新的应用-The SVM-based classification algorithm is a kind of new application in the field of artificial intelligence and pattern recognition.
Platform: | Size: 67430 | Author: rabo | Hits:

[Windows Developseveral-SVMs-algorithms

Description: 支持向量机几个算法源代码 包括以下算法 cSVM3.1.8 libsvm-2.88 newsvm OnlineSVR_C--_Code svmcla-cSVM3.1.8 libsvm-2.88 newsvm OnlineSVR_C--_Code svmclass
Platform: | Size: 4442112 | Author: zidianwjq | Hits:

[AI-NN-PRpython-2.6.6

Description: python2.6-6安装文件,用于使用libsvm的.py文件。-installation file of python
Platform: | Size: 14734336 | Author: 田乐逍 | Hits:

[matlabCODE

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: | Size: 6918144 | Author: 张数 | Hits:

[AI-NN-PRlibsvm-mat-2.8-1

Description: 有良好的分类能力,接口函数易用,是一个完整c环境下开发的libsvm,比svm分类效果好。- Have good ability of classification,Interface function is easy to use。Is a complete c environment of development libsvm, SVM classification than effect is good.
Platform: | Size: 109568 | Author: apollo | Hits:

[Shell apitmsvm_for_win_1.2.0

Description: window下的文本分类工具tmSVM,该系统在封装 libsvm 、 liblinear 的基础上,又增加了 特征选择 、 LSA特征抽取 、 SVM模型参数选择 、 libsvm格式转化模块 以及一些实用的工具。-Text categorization tool tmSVM under the window, the system on the basis of encapsulation libsvm, liblinear, added feature selection, LSA feature extraction, the SVM model parameter selection, libsvm format conversion module As well as some practical tool.
Platform: | Size: 3625984 | Author: kailun | Hits:

[AI-NN-PRlibsvm-3.1

Description: LIBSVM is an integrated software for support vector classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification. Since version 2.8, it implements an SMO-type algorithm proposed in this paper: R.-E. Fan, P.-H. Chen, and C.-J. Lin. Working set selection using second order information for training SVM. Journal of Machine Learning Research 6, 1889-1918, 2005. You can also find a pseudo code there. (how to cite LIBSVM) Our goal is to help users other fields to easily use SVM as a tool. LIBSVM provides a simple interface where users can easily link it with their own programs. Main features of LIBSVM include-LIBSVM is an integrated software for support vector classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification. Since version 2.8, it implements an SMO-type algorithm proposed in this paper: R.-E. Fan, P.-H. Chen, and C.-J. Lin. Working set selection using second order information for training SVM. Journal of Machine Learning Research 6, 1889-1918, 2005. You can also find a pseudo code there. (how to cite LIBSVM) Our goal is to help users other fields to easily use SVM as a tool. LIBSVM provides a simple interface where users can easily link it with their own programs. Main features of LIBSVM include
Platform: | Size: 1321984 | Author: carl2380 | Hits:

[AI-NN-PRlibsvm-3.22

Description: libsvm-3.22.rar LIBSVM is an integrated software for support vector classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification. Since version 2.8, it implements an SMO-type algorithm proposed in this paper: R.-E. Fan, P.-H. Chen, and C.-J. Lin. Working set selection using second order information for training SVM. Journal of Machine Learning Research 6, 1889-1918, 2005. You can also find a pseudo code there. (how to cite LIBSVM) Our goal is to help users other fields to easily use SVM as a tool. LIBSVM provides a simple interface where users can easily link it with their own programs. Main features of LIBSVM include-libsvm-3.22.rar LIBSVM is an integrated software for support vector classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification. Since version 2.8, it implements an SMO-type algorithm proposed in this paper: R.-E. Fan, P.-H. Chen, and C.-J. Lin. Working set selection using second order information for training SVM. Journal of Machine Learning Research 6, 1889-1918, 2005. You can also find a pseudo code there. (how to cite LIBSVM) Our goal is to help users other fields to easily use SVM as a tool. LIBSVM provides a simple interface where users can easily link it with their own programs. Main features of LIBSVM include
Platform: | Size: 839680 | Author: carl2380 | Hits:
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