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Description: EM算法处理高斯混和模型，是用MATLAB实现的
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Size: 1587 |
Author: 李晋博 |
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Description: 用em算法对数值进行分类，能到达一定的效果
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Size: 5480 |
Author: 吴杰 |
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Description: The EM algorithms and extensions. 作者Geoffery J. Mclachlan
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Size: 9289122 |
Author: aannzz |
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Description: EM算法处理高斯混和模型，是用MATLAB实现的-EM algorithm for Gaussian mixture model of treatment is achieved using MATLAB
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Size: 1024 |
Author: 李晋博 |
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Description: 用em算法对数值进行分类，能到达一定的效果-Em algorithm with numerical classification, able to get certain effects
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Size: 5120 |
Author: 吴杰 |
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Description: EM算法，算法介绍，详细，上手快！好学好用好记，通俗易懂，简单上手-EM algorithm introduction, detailed, on手快! Learn-to-use easy to remember, easy to understand, easy to use! ! !
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Size: 617472 |
Author: alton |
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Description: 全概率公式和贝叶斯公式 全概率公式和贝叶斯公式 -全概率公式和贝叶斯公式全概率公式和贝叶斯公式全概率公式和贝叶斯公式全概率公式和贝叶斯公式
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Size: 199680 |
Author: zengqinghui |
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Description: 详细介绍了EM算法的推到及使用，是学习图像处理，图像分割的一篇经典。-Details of the EM algorithm is pushed to and use of learning image processing, image segmentation of a classic.
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Size: 930816 |
Author: 本杰明 |
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Description: 最大的高斯混合模型似然估计的期望最大化算法-Maximum likelihood estimation of Gaussian mixture model by expectation maximization algorithm
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Size: 19456 |
Author: ken |
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Description: EM算法的详细介绍（含PDF文件）及其matlab实现-EM algorithm is a detailed description (including PDF files) and its implementation matlab
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Size: 118784 |
Author: 李静 |
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Description: 上数据挖掘课的课件，是EM算法的，其中还包括最大似然值，最大似然估计，以及cluster-data mining,EM Algorithm ,Likelihood, Mixture Models and Clustering
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Size: 514048 |
Author: Erin |
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Description: EM算法 EM算法 EM算法-EMAlgorithmEMAlgorithmEMAlgorithmEMAlgorithm
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Size: 617472 |
Author: mahz |
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Description: 书中详细介绍了EM算法的由来，及其迭代算法。-sorry my english is poor
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Size: 617472 |
Author: 庞善民 |
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Description: 详细描述EM算法的文章以及课件，很难找到的-very difficult to find .very perfect
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Size: 867328 |
Author: 董巍 |
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Description: In statistics, an expectation-maximization (EM) algorithm is a method for finding maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. EM is an iterative method which alternates between performing an expectation (E) step, which computes the expectation of the log-likelihood evaluated using the current estimate for the latent variables, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. These parameter-estimates are then used to determine the distribution of the latent variables in the next E step.-In statistics, an expectation-maximization (EM) algorithm is a method for finding maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. EM is an iterative method which alternates between performing an expectation (E) step, which computes the expectation of the log-likelihood evaluated using the current estimate for the latent variables, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. These parameter-estimates are then used to determine the distribution of the latent variables in the next E step.
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Size: 2048 |
Author: loossii |
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Description: 运用EM算法生成任意条件下的二维高斯分布(Using the EM algorithm to generate a two-dimensional Gaussian distribution in any condition)
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Size: 2048 |
Author: 刘少博 |
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