搜索资源列表
clusters
- 该算法包包含了hierichal，kmeans，em聚类算法，非常实用。
EMGMM
- 混合高斯模型和EM算法结合，当中用到了自己写的Kmeans聚类，附带测试样例、训练样例和main函数。
KMEANS01
- This directory contains code implementing the K-means algorithm. Source code may be found in KMEANS.CPP. Sample data isfound in KM2.DAT. The KMEANS program accepts input consisting of vectors and calculates the given
EM
- 用matlab语言写的EM(Expectation maximization)算法,用于模式分类-Matlab language used to write the EM (Expectation maximization) algorithm for pattern classification
clusters
- 该算法包包含了hierichal，kmeans，em聚类算法，非常实用。-The algorithm package contains hierichal, kmeans, em clustering algorithm is very practical.
EMGMM
- 混合高斯模型和EM算法结合，当中用到了自己写的Kmeans聚类，附带测试样例、训练样例和main函数。-Gaussian Mixture Model and EM algorithms, which use their own written Kmeans cluster, with the test sample, the training sample and the main function.
kmeans
- 用matlab实现的kmeans算法，是一个后台程序，没有调用其本身的kmeans算法，挺不错的-Using matlab to achieve kmeans algorithm, is a background process, not to call its own kmeans algorithm, very good
EMarithmeticprogramandexperimentalreport
- EM算法源程序的代码，运行的结果和实验报告。-EM algorithm source code, operating results and the experimental report.
Clustering
- duke的tutorial on EM的matlab经典源码，值得一看。-Matlab code for the tutorial on Expectation Maximization，worth a visit.
EM-kmeans
- 用matlab实验k-means算法，对数据进行分类。-Matlab experiments using k-means algorithm to classify the data.
EM_GM
- EM算法实现,是matlab主站上获得的可靠程序。-EM algorithm，which is from the matlab net about image processing.
EMAlgorithm
- EM算法的详细介绍（含PDF文件）及其matlab实现-EM algorithm is a detailed descr iption (including PDF files) and its implementation matlab
EMgaussian
- EM算法求高斯过程的参数,这种算法的计算过程简单，但是准确度不高-EM algorithm for getting the parameters of Gaussian process, this computation is simple, but accuracy is not high
MOG-module-to-model-data-
- VS2010及IT++4.2下实现MOG的仿真-This example demonstrates how to find the parameters of a MOG model via using the kmeans and EM based optimisers. Synthetic data is utilised.
MriSeg
- MRI 脑组织参数估计与分割。此程序用两种方法——Kmeans和期望最大化EM对比对MRI脑组织进行分割和参数估计-The MRI parameter estimation and segmentation of brain tissue. This program on MRI brain tissue segmentation and parameter estimation using two methods-- Kmeans a
Kmeans_VS_EM_OnLaborDataSet
- 使用著名的数据挖掘和机器学习软件WEKA，在标准数据集labor上，比较Kmeans与EM算法。-Use well-known data mining and machine learning software WEKA, on standard data sets labor, relatively Kmeans with EM algorithm.
EM
- 用kmeans初始化em算法，并用高斯回归进行拟合，用于图想的聚类-Kmeans initialization using EM algorithm, and the use of Gauss regression fitting, used to map the clusterin
peel-lerher
- 混合高斯模型和EM算法结合,当中用到了自己写的Kmeans聚类,附带测试样例,训练样例和main函数,()
yssr
- 混合高斯模型和EM算法结合,当中用到了自己写的Kmeans聚类,附带测试样例,训练样例和main函数,()
machine_learning_python-master
- 通过阅读网上的资料代码，进行自我加工，努力实现常用的机器学习算法。感知机的基本形式和对偶形式的实现 Kmeans和Kmeans++的实现 EM GMM高斯混合和GMM+LASSO的实现 实现朴素贝叶斯的基本算法和高斯混合朴素贝叶斯算法 实现决策树的基本算法 实现adaboost基本算法 实现svm基本算法 实现逻辑回归基本算法(By reading the data codes on the I