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Expectation-Maximization
The EM (Expectation-Maximization) algorithm estimates the parameters of the multivariate probability density function in a form of the Gaussian mixture distribution with a specified number of mixtures.
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Source code - create Gaussian Mixture Model in following steps:
1, K-means
2, Expectation-Maxximization
3, GMM
Notice: All datapoints are generated randomly and you can config in Config.h-Source code- create Gaussian Mixture Model
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文章展示了基于高斯混合模型的语音频谱预测方法。频谱预测可能在传包过程中预防丢包这方面起到大作用。期望最大化算法用两倍或三倍的连续语音因素来测试模型。模型被用来设计第一，儿等指令预测量。预测表用频谱分配状态来估计并和一个简单的参考模型对比。最好的预测表得到一个平均频率扭曲值是0.46dB小于参考模型-This paper presents methods for speech spectrum prediction based
on Gaussian mixture models. Spec
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This introduction to the expectation–maximization (EM) algorithm
provides an intuitive and mathematically rigorous understanding of
EM. Two of the most popular applications of EM are described in
detail: estimating Gaussian mixture models (GMMs),
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这篇论文基于Kernel和Gaussian Mixture Model提出了一种新的误码率估计方法，使用Stochastic Expectation-Maximization算法实现误码率实时盲估计-This report presents a new Bit Error Rate estimation method based on Kernel and Gaussian Mixture Model, the method utilizes Stochastic Expectation-Max
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