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A general framework for solving image inverse problems is introduced in this paper. The approach is based on Gaussian mixture models, estimated via a computationally efficient MAP-EM algorithm.
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a document about "Baseball Playfield Segmentation Using Adaptive Gaussian Mixture Models"
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very good Gaussian Mixture Models and Probabilistic Decision-Based Neural Networks for Pattern Classification - A Comparative Study document -very good Gaussian Mixture Models and Probabilistic Decision-Based Neural Networks for Pattern Classificatio
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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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The document consists of explanation for gmm ie Gaussian mixture models
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