搜索资源列表
用matlab实现K均值聚类算法
- 用matlab实现K均值算法
matlab信号处理工具箱代码
- matlab信号处理的基础源代码，如：比较相参积累和非相参积累、雷达的恒虚警处理（CFAR）、相干相关K分布杂波、等等，总共有上百个m文件。
matlab K近邻分类法
- matlabK近邻法实现
K-均值聚类算法
- K-均值聚类算法，对数据进行聚类分析，可用于提取关键帧等。用matlab实现,K-means clustering algorithm, cluster analysis of data that can be used, such as key frame extraction. Using matlab to achieve
基于matlab的K均值聚类程序
- 基于matlab的K均值聚类程序。其中用IRIS数据进行验证，得到了很好的结果。文件中包含了演示后的结果图,Matlab-based K-means clustering procedure. Which use IRIS data verification, have had good results. File contains the results of the demonstration plan
在matlab下的k-均值聚类进行图像分类分割处理
- 在matlab下的k-均值聚类进行图像分类分割处理,In matlab under the k-means clustering for image classification be dealt with separately
kMedoids.rar k-中心聚类算法的matlab实现
- k-中心聚类算法的matlab实现。直接读取文档数据，没有维限制。,k-Medoids clustering algorithm matlab implementation. Document data read directly, there is no dimension restrictions.
MeanShiftCluster.zip
- Mean shift clustering. K means clustering.,Mean shift clustering. K means clustering.
K-MEANS-MATLAB
- 用matlab7.0编写的k均值算法，参数可调节，很好用-K-MEANS MATLAB
K-means
- 简单实用的k均值聚类算法，可以实现多位向量的简单聚类-Simple and practical k-means clustering algorithm, can achieve more than a simple vector clustering
cluster
- k均值聚类算法源码（matlab） k均值聚类算法源码（matlab）-k-means clustering algorithm source code (matlab) k-means clustering algorithm source code (matlab)
K-Means
- 较简单的KMeans聚类算法实现，编程语言matlab-Clustering KMeans relatively simple algorithm, programming language matlab
K-means_Matlab
- K-均值算法的Matlab源代码，比较简短-Matlab source code of K-means algorithm
k-means
- k均值聚类算法源码 聚类算法学习的实例功能-k-means cluster algorithm
k-centers
- 不同于k均值聚类的k中心聚类，2007年SCIENCE文章Clustering by Passing Messages Between Data Points 中的方法-Unlike k-means clustering of the k cluster centers, in 2007 SCIENCE article, Clustering by Passing Messages Between Data Points of the Method
K-means.m
- K-mean均值算法的matlab功能实现-K-means to achieve the matlab function
K-Means-Color-Reduction
- 基于K-MEANS的图像退色算法。平台为MATLAB6.5及以上。-Based on the K-MEANS algorithm image fade
K-Fold_CV_Tool
- MATLAB cross-validation tool for classification and regression v0.1 FEATURES: + K-fold cross validation. + Arbitrary train and prediction functions with parameters can be used. + Arbitrary loss function can be used. + Wrappers for
k-medoids
- k-medoids聚类算法对数据进行分类处理(k-medoids Clustering algorithm for data classification)
K—均值聚类提取
- k均值聚类提取，适合学习。先将RGB图像转换到LAB空间，在LAB空间进行聚类分割。(K-means clustering is suitable for learning. First convert the RGB image to LAB space and perform clustering and segmentation in the LAB space.)