Prang

积分:234
上传文件:6
下载次数:221
注册日期:2015-01-24 22:23:28

上传列表
PCA_for_classification-master.zip - PCA Classification learning,2020-12-30 17:45:38,下载0次
C4_5.rar - matlab decision tree c4.5,2017-06-29 12:02:53,下载4次
resample.rar - Resample dataset for learning with adaboost/boosting ,2016-02-10 14:38:30,下载2次
adaboost-1.zip - Matlab Adaboost for 2 class which function,2016-02-10 14:31:59,下载3次
confusionmatSen.zip - example fi e for calculate sensitivity by confusion matrix,2015-09-20 00:12:25,下载4次
code-kmean.rar - K-mean matlab code example,2015-01-24 22:25:50,下载3次

近期下载
Code_SKFCM.zip - Matlab code for SKFCM for segmentation
AdaBoost(original).rar - AdaBoost(original),普通的AdaBoost算法的Matlab实现
FCM.zip - 模糊C均值算法,FCM算法,MATLAB程序,可运行
Fuzzy c-means algorithm .rar - 用模糊C均值聚类算法完成对输入数据的聚类。
PCA.rar - 模式识别作业-完全自编仿真程序。先用PCA对IRIS数据集进行降维,然后用最小错误法对降维的数据进行分类。压缩包中既包括matlab源代码,又有自己写的报告,还有.MAT格式的IRIS数据集用作程序调用。程序有详细注释,很容易懂。最后结果输出到txt文件中。
LDA-PCA-classifier.rar - this program will help you to perform LDA and PCA signal processing tools for classifing
classification.rar - 实现PCA分类.1、进行PCA的交叉检验。2、对数据进行PCA降维。3、进行分类,交叉检验。4、构造训练和测试的数据
1087.rar - pca+svm源代码(matlab) matlab代码,pca进行特征提取,svm进行分类
K-Mean Clustering.rar - k means clustering with matlab code
K-means.zip - 用matlab实现k均值算法,不用库函数
Kmeans.rar -  Kmeans 算法是聚类分析中使用最为广泛的算法之一,其每个类别均用该类中所有数据的平均值(或加权平均)来表示,这个平均值即被称作聚类中心。该方法虽然不能用于 类别属性的数据,但对于数值属性的数据,它能很好地体现聚类在几何和统计学上的意义。
test_kMeansCluster.m.rar - K mean cluster matlab code
Machine-Learning-for-IoT-master.zip - mL for IoT masters in matlab code
adaboost.rar - Now, you ought to implement the AdaBoost.M1 and AdaBoost.M2 algorithms. These algorithms are two versions of the AdaBoost algorithm for handling the Problems with more than two classes. You must first read the paper “Experiments with a New Boosting Algorithm”. Use decision stump and C4.5 classifiers of Weka as the base classifiers for AdaBoost.M1 and use decision stump as the base classifier for AdaBoost.M2.
QDA.zip - 使用自己写的pca实现qda的多项式分类,没有使用工具包,全部自写
DiscriminantAnalysis.rar - Implementation to linear, quadratic and logistic discriminant analysis, for examples

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