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机器学习中的E M算法，本代码是基于高斯混合模型的E M 算法聚类。-machine learning algorithm E M, the code is based on the Gaussian mixture model clustering algorithm E. M.
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Probability distribution functions.
estimation - (dir) Probability distribution estimation.
dsamp - Generates samples from discrete distribution.
erfc2 - Normal cumulative distribution function.
gmmsamp - Generates sample from Gaussian m
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基于EM算法的模型聚类的研究及应用，GMM高斯混合模型-EM-based clustering algorithm and its application model, GMM Gaussian mixture model
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LBG分类算法
用初始室心随机法和扰动因子分裂法两种方法，比较不同方法不同参数设置时的分类性能。
-LBG classification algorithm vector quantization: vector normalization within a certain range for a particular type, consists of two steps: first generate a codebook, which is the speech feature v
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介绍期望最大算法基本原理及聚类实现，可以很好的对多个高斯概率密度分布进行分类-Introduces the basic principle and expectation maximization clustering algorithm to achieve, can be good for multiple Gaussian probability density distribution of the classification
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聚类算法之高斯混合模型，GMM 和 k-means 很像，不过 GMM 是学习出一些概率密度函数来（所以 GMM 除了用在 clustering 上之外，还经常被用于 density estimation ）。-Gaussian mixture model of clustering algorithm, GMM and k-means like, but GMM is learning some probability density function (so GMM except on cl
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