matlab-examples

说明：  基于遗传算法的图像阈值分割。里面有代码和本人搜集的参考文献，有助于初学者学习。(Based on genetic algorithm for image thresholding. I gathered codes and references which are very helpful for beginners.)

包含不同条件下的MAB问题 仿真，还有仿真结果图例(include MAB PROBLEM policyBayesUCB.m policyCPUCB.m policyDMED.m policyGittins.m policyKLUCB.m )

It is related to image processing

单次快拍实现DOA的估计，“Modified UCA-ESPRIT and Modified UCA-ROOT-MUSIC for Estimating Doa of Coherent Signals Using One Snapshot.pdf”文章的配套代码(Modified UCA-ESPRIT and Modified UCA-ROOT-MUSIC for Estimating Doa of Coherent Signals Using One Snapshot.pdf”its code)

A set of Matlab experiments that illustrates advanced computational signal and image processing.

最小冗余最大相关性（MRMR）（MRMR.M） 需要外部库。详情请见MRMR。下载一个更新版本的互信息工具箱 偏最小二乘（PLS）回归系数（ReGCOEF.m） 使用MATLAB统计工具箱中的PLSReress ReliefF（分类）和RReliefF（回归）（ReleFracePr.M.） 从Matlab STATS工具箱中包装Releff.m。这是Matlab R2010B以后提供的。 ReliefF的另一个选择是使用ASU特征选择工具箱中的代码。这使用WEKA工具箱的ReleFEF，因此需要额外的库。请参阅相应的文档。 费雪评分（Fisher评分） 围绕ASFS特征选择工具箱围绕FSFisher。M(Minimum Redundancy Maximum Relevance (mRMR) (mRMR.m) Needs external library. See mRMR.m for details. Download a newer version of the mutual information toolbox Partial Least Squares (PLS) regression coefficients (regCoef.m) Uses plsregress.m from MATLAB statistics toolbox ReliefF (classification) and RReliefF (regression) (relieffWrapper.m) Wraps around relieff.m from the MATLAB stats toolbox. This is available MATLAB r2010b onwards. Another option for ReliefF is to use the code from ASU Feature Selection toolbox. This uses ReliefF from weka toolbox and hence needs additional libraries. Please see the corresponding documentation. Fisher Score (fisherScore.m) Wraps around fsFisher.m from the ASU Feature Selection toolbox)

使用MATLAB生成棋盘矩阵,希望对大家有帮助(Use MATLAB to generate chessboard matrix, in the hope that everyone has to help)

基于二阶统计量的SOBI算法，适用于盲源分离(SOBI algorithm based on second-order statistics for blind source separation)

上帝算法是优化算法中一种比较特殊的算法，与哲学心理学有关联。(God s Algorithm)

This package enables Gaussian process prediction with very large sample sizes with a much smaller computational burden than many existing methods.