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BRISQUE_release
- This a demonstration of the Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) index. The algorithm is described in: A. Mittal, A. K. Moorthy and A. C. Bovik, No Reference Image Quality Assessment in the Spatial Domain Binaries:
vad_directed_by_noise_classification
- vad_directed_by_noise_classification.m This code is an implementation of VAD algorithm proposed in: Robust voice activity detection directed by noise classification please cite the article in your paper: Robust voice activity detec
mutli-output
- 多输出的支持向量机matlab代码, 常规的svm是单输出，这个是多输出- Standard SVR formulation only considers the single-output problem. In the case of several output variables, other methods (neural networks, kernel ridge regression) must be deployed, but the good properties of
mysvm
- SVM,支持向量机，matlab 实现，具体实现了SMO算法，线性核，高斯核-SVM,support vector machine ,matlab implement m, In details ,implement SMO algorithm,linear kernel ,RBF kernel
rough-set-codes
- 这是天津大学胡清华老师在粗糙集邻域领域做的最经典的源码，同学们可以在此基础上学习和修改，入口程序已经写好，需要其他方法可以自己添加，MAIN.m是入口程序，参数的意思在子函数里讲的很明白，调用了featureselect_FW_fast.m用来属性约简，几个clsf_dpd文件是使用不同的距离公式来计算属性重要度，选择得到属性结果，使用crossvalidate.m十折交叉算法来计算计算算法精度，该段代码调用了几个分类器，C4_5.m是决策树，KNN.m是最近邻分类器，NEC.m是类似于KNN的
SVM_lzb1p0
- 这里实现了四种SVM工具箱的分类与回归算法 1、工具箱：LS_SVMlab Classification_LS_SVMlab.m - 多类分类 Regression_LS_SVMlab.m - 函数拟合 2、工具箱：OSU_SVM3.00 Classification_OSU_SVM.m - 多类分类 3、工具箱：stprtool\svm Classification_stprtool.m - 多类分类 4、工具箱：SVM_SteveGunn Classification_SVM_SteveGu
huipie_v67
- 包括最小二乘法、SVM、神经网络、1_k近邻法,已经调试成功.内含m文件，可直接运行，这是第二能量熵的matlab代码。- Including the least squares method, the SVM, neural networks, 1 _k neighbor method, Has been successful debugging. M contains files can be directly run, This is the second energy entropy m
stprtool
- 工具箱：stprtool\svm Classification_stprtool.m - 多类分类-it is a stptool
loutun_v79
- 已经调试成功.内含m文件，可直接运行，包括最小二乘法、SVM、神经网络、1_k近邻法,包括广义互相关函数GCC时延估计。- Has been successful debugging. M contains files can be directly run, Including the least squares method, the SVM, neural networks, 1 _k neighbor method, Including the generalized cross-cor
chapter_PSO
- 用psoSVMcgForClass.m实现对分类问题用PSO来优化SVM参数的问题-Use psoSVMcgForClass. M for classification problems using PSO to optimize parameters of SVM
Classification_stprtool
- stprtool\svm Classification_stprtool.m - 多类分类-Stprtool \ svm Classification_stprtool.m- u591A u7C7B u5206 u7C7B
SMO算法实现
- 使用Matlab重新实现了svm算法中的核心算法SMO，即序列优化问题。 其中my_seqminopt.m是重写的实现代码，完成了核心的迭代优化过程。(MATLAB code of SMO algorithm in SVM algorithm)
New folder
- Multi_Label_SVM. in this M file we implement multi label svm
Data
- osu-svm是一个基于libsvm的matlab工具箱,分为c-svm和nu-svm,本质上差不多,c-svm中c的范围是1到正无穷,nu-svm中nu的范围是0到1,nu是错分(ComputeSampEnSVM_Toolbox\osu_svm3.00\cmap.mat SVM_Toolbox\osu_svm3.00\Contents.m SVM_Toolbox\osu_svm3.00\demo\c_clademo.m SVM_Toolbox\osu_svm3.00\demo\c_
classifier
- 一些分类器尝试，包括SVM，KNN，自带树与adaboost或者bagging结合等。(Some classifiers test,such as SVM,KNN,etc, including test data. Only some of the methods are included in the main.m.)