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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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统计模式识别工具箱（Statistical Pattern Recognition Toolbox）包含：
1，Analysis of linear discriminant function
2，Feature extraction: Linear Discriminant Analysis
3，Probability distribution estimation and clustering
4，Support Vector and other Kernel Machines,
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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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奉文主要针对经典的Mean Shift跟踪算法均匀剖分整个颜色空间造成许多空的直方图区间以及不能准确表达目标
颜色分布的缺点，提出J，一种改进算法．该改进算法首先对目标的颜色进行聚类分析，根据聚类结果通过矩阵分解和正交变换
自适席地剖分日标的颜色空间从向确定对戍于每一聚类的子空间．在此基础上定义 一种新的颜色模型，该模型统计落入每
一颜色子空间的像素的加权个数并用高斯分布建模每一个子空间的颜色分布，并推导r一种相似性度量米比较目标和候选目
标的颜色模型之间的相似程度．最后基于该颜
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高斯k均值程序算法，k-means是一种常用的聚类算法，可以实现数据的聚类。-K-means algorithm for Gaussian process, k-means clustering algorithm is a commonly used, can achieve data clustering.
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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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使用K-means，混合高斯模型（GMM），层次聚类算法实现的多类别数据的聚类。内含详细的实验报告。-Using K-means, Gaussian mixture model (GMM), hierarchical clustering algorithm to achieve multi-class data clustering. Including a detailed lab report.
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KMEAN C#
In data mining, k-means clustering is a method of cluster analysis which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean. This results in a partitioning of the data sp
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EM高斯混合多变量模型聚类算法，直接可以运行-EM Gaussian mixture model clustering algorithm, and can be run directly
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一种EM算法实例，用于混合高斯分布聚类处理数据-One kind of example of the EM algorithm for clustering mixed Gaussian process data
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实现EM算法的MATLAB仿真程序，利用高斯混合模型实现EM聚类算法，并比较估计参数。-EM algorithm to achieve the MATLAB simulation program, using Gaussian Mixture Model EM clustering algorithm, and compare the estimated parameters.
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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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实现利用高斯分布聚类，即完成em算法，直接在matlab上运行，可以作图直观显示（二维数据）-Achieve Gaussian distribution clustering, complete em algorithm, running directly on matlab, you can visualize mapping (two-dimensional data)
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This paper presents a new approach to image segmentation using Pillar K-means algorithm. This
segmentation method includes a new mechanism for grouping the elements of high resolution images in order to
improve accuracy and reduce the computation
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经典的em算法即期望最大化算法，可用于高斯混合GMM模型和聚类算法，-Classic em algorithm that expectation maximization algorithm can be used for Gaussian mixture models and GMM clustering algorithm,
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基于EM算法实现的高斯混合模型数据分类，可以很优秀的对各种数据进行聚类分析，R语言实现-EM algorithm based on Gaussian mixture model data classification, can be very good for a variety of data clustering analysis, R language
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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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Just like EM of Gaussian Mixture Model, this is the EM algorithm for fitting Bernoulli Mixture Model.
GMM is useful for clustering real value data. However, for binary data (such as bag of word feature) Bernoulli Mixture is more suitable.
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基于K均值的PSO聚类算法,计算加权加速度,高斯白噪声的生成程序。- K-means clustering algorithm based on the PSO, Weighted acceleration, Gaussian white noise generator.
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