搜索资源列表
GMM_EM
- E-step and M-step in G-E-step and M-step in GMM
GMM_EM
- 一种高斯混合模型的参数训练方法，主要应用高斯混合模型的参数训练-Parameters of a Gaussian mixture model training method, the main application of the parameters of the Gaussian mixture model training
GMM_EM
- 2类分类高斯混合模型 使用k-means的方法来初始化GMM， 基于EM算法计算出GMM模型参量。 测试GMM模型分别有2个，4个，8个混合成分-2-class classifier with Gaussian Mixture Models. Use the k-means method to initialize the GMM’s Then improve the GMM models iteratively b
GMM_EM
- 高斯混合模型的EM搭建过程以及详细说明，利于初学者的学习！-EM Gaussian mixture model building process as well as detailed instructions, which will help beginners to learn!
GMM_EM
- 高斯混合模型EM算法,通过EM算法来进行高斯混合模型的参数估计-Gaussian mixture model EM algorithm parameters by EM algorithm to estimate the Gaussian mixture model
GMM_EM
- 使用高斯混合模型和最大似然估计结合的方法实现语音信号特征的训练和识别-Using GMM method to realize voice signal characteristics of training and recognition
GMM
- gmm_em算法，可用于手写数字识别，供初学者参考。-Gmm_em algorithm and can be used for handwritten numeral recognition, for beginners reference.
GMM_EM
- EM算法的实现，采用工具包中krkonose数据及随机生成的二维数据进行测试和学习。-Realization of the EM algorithm, using the toolkit krkonose data and two-dimensional data randomly generated test and learn.
GMM_EM
- GMM算法是混合高斯模型，其求解过程需要不断迭代，本程序利用EM算法进行了仿真实现，可以加深对GMM的理解。(GMM algorithm is a hybrid Gauss model, and its solution process needs iteration. This program uses EM algorithm for simulation, which can deepen the understanding of
GMM_EM
- 混合高斯模型的参数计算方法，采用EM迭代的方法求得(Parameter calculation method of mixed Gauss model)
GMM
- matlab 实现GMM——EM算法,自动生产混合高斯分布，GMM算法的示例demo(matlab em gmm,Automatic production of mixed Gauss distribution, an example of GMM algorithm demo)
GMM_EM
- GMM模型下的EM算法，一个实用的matlab仿真代码(EM algorithm under GMM model)