模拟EM卡程序，可以与EM4100通用。也可以做为无线传输时使用！
上传时间： 2016-04-08
上传用户：gyq
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。
上传时间： 2013-12-23
上传用户：zhangyi99104144
this a 8-bit risc micro process,Th eM C Ud esignedis c ompatiblew ith PIC16C57 o microchip Technology Inc.in the instruction system
标签： ompatiblew Technolog esignedis microchip
上传时间： 2014-01-14
上传用户：xinyuzhiqiwuwu
In this demo, I use the EM algorithm with a Rauch-Tung-Striebel smoother and an M step, which I ve recently derived, to train a two-layer perceptron, so as to classify medical data (kindly provided by Steve Roberts and Will Penny from EE, Imperial College). The data and simulations are described in: Nando de Freitas, Mahesan Niranjan and Andrew Gee Nonlinear State Space Estimation with Neural Networks and the EM algorithm After downloading the file, type "tar -xf EMdemo.tar" to uncompress it. This creates the directory EMdemo containing the required m files. Go to this directory, load matlab5 and type "EMtremor". The figures will then show you the simulation results, including ROC curves, likelihood plots, decision boundaries with error bars, etc. WARNING: Do make sure that you monitor the log-likelihood and check that it is increasing. Due to numerical errors, it might show glitches for some data sets.
标签： Rauch-Tung-Striebel algorithm smoother which
上传时间： 2016-04-15
上传用户：zhenyushaw
本文介绍了用c++实现em算法，非常有用！
标签： 算法
上传时间： 2014-11-30
上传用户：磊子226
高斯混合模型参数估计,EM算法,sunMOG.m为函数,testMOG4.m为测试程序
上传时间： 2014-03-09
上传用户：电子世界
一个很有用的EM算法程序包，可用于混合高斯模型，值得一看哦
上传时间： 2016-04-28
上传用户：llandlu
51单片机读EM卡的程序,EM卡输出是曼彻斯特吗有.很好用的.
上传时间： 2016-05-19
上传用户：luke5347
EM分群,matlab程式碼,用來分群用的
上传时间： 2013-11-25
上传用户：Andy123456
％ EM algorithm for k multidimensional Gaussian mixture estimation ％ ％ Inputs: ％ X(n,d) - input data, n=number of observations, d=dimension of variable ％ k - maximum number of Gaussian components allowed ％ ltol - percentage of the log likelihood difference between 2 iterations ([] for none) ％ maxiter - maximum number of iteration allowed ([] for none) ％ pflag - 1 for plotting GM for 1D or 2D cases only, 0 otherwise ([] for none) ％ Init - structure of initial W, M, V: Init.W, Init.M, Init.V ([] for none) ％ ％ Ouputs: ％ W(1,k) - estimated weights of GM ％ M(d,k) - estimated mean vectors of GM ％ V(d,d,k) - estimated covariance matrices of GM ％ L - log likelihood of estimates ％
标签： multidimensional estimation algorithm Gaussian
上传时间： 2013-12-03
上传用户：我们的船长
