编译通过的迭代算法ARTII程序

猴子摘桃海滩上有一堆桃子，五只猴子来分。第一只猴子把这堆桃子凭据分为五份，多了一个，这只 猴子把多的一个扔入海中，拿走了一份。第二只猴子把剩下的桃子又平均分成五份，又多了 一个，它同样把多的一个扔入海中，拿走了一份，第三、第四、第五只猴子都是这样做的， 问海滩上原来最少有多少个桃子？(Peach Monkeys on the beach there is a pile of peaches, five monkeys for points. The first monkey peach credentials this heap is divided into five copies, more than one, the monkey to more than one thrown into the sea, took a. The second monkey the rest of the peaches and what the average is divided into five, and there are one, and it is likewise more than one thrown into the sea, took a third, fourth, fifth monkeys are actually doing that , the question on the beach for at least the number of the original peach?)

rake的matlab程序，经典的rake仿真程序(rake in Matlab procedures, the classic rake simulation program)

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 Download a newer version of the mutual information toolbox Partial Least Squares (PLS) regression coefficients (regCoef.m) Uses plsregress.m from MATLAB statistics toolbox ReliefF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)

Description This a classic AdaBoost implementation, in one single file with easy understandable code. The function consist of two parts a simple weak classifier and a boosting part:  The weak classifier tries to find the best threshold in one of the data dimensions to separate the data into two classes -1 and 1  The boosting part calls the classifier iteratively, after every classification step it changes the weights of miss-classified examples. This creates a cascade of "weak classifiers" which behaves like a "strong classifier"  .  Training mode:    [estimateclass,model]=adaboost("train",datafeatures,dataclass,itt)  Apply mode:   estimateclass=adaboost("apply",datafeatures,model)     inputs/outputs:     datafeatures : An Array with size number_samples x number_features     dataclass : An a

MATLAB下的振动仿真 本科毕业设计，要的下(zhedongfang zhe matlab fdf)

meshless method programme for moving least square approximation

matlab code is very good one

伪随机序列相关性、功率谱、列数以及频数等的分析(Analysis of pseudo-random sequence)

跟踪连续图像序列中的运动目标--小白车(meanshift code)

Implémentation d une Table de Huffman statique