模擬EM卡程序，可以與EM4100通用。也可以做為無線傳輸時使用！
上傳時間： 2016-04-08
上傳用戶：gyq
混合高斯模型和EM算法結合，當中用到了自己寫的Kmeans聚類，附帶測試樣例、訓練樣例和main函數。
上傳時間： 2013-12-23
上傳用戶：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
標簽： ompatiblew Technolog esignedis microchip
上傳時間： 2014-01-14
上傳用戶：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.
標簽： Rauch-Tung-Striebel algorithm smoother which
上傳時間： 2016-04-15
上傳用戶：zhenyushaw
本文介紹了用c++實現em算法，非常有用！
標簽： 算法
上傳時間： 2014-11-30
上傳用戶：磊子226
高斯混合模型參數估計,EM算法,sunMOG.m為函數,testMOG4.m為測試程序
上傳時間： 2014-03-09
上傳用戶：電子世界
一個很有用的EM算法程序包，可用于混合高斯模型，值得一看哦
上傳時間： 2016-04-28
上傳用戶：llandlu
51單片機讀EM卡的程序,EM卡輸出是曼徹斯特嗎有.很好用的.
上傳時間： 2016-05-19
上傳用戶：luke5347
EM分群,matlab程式碼,用來分群用的
上傳時間： 2013-11-25
上傳用戶：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 ％
標簽： multidimensional estimation algorithm Gaussian
上傳時間： 2013-12-03
上傳用戶：我們的船長
