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gem算法
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  gem algorithm
     Handles the problem of parameter estimation for truncated samples from normal populations via the EM algorithm. In M step we propose a modifications of the algorithm. As shown in a real problem and computer simulation, this procedure works well and belongs to the GEM algorithm.
     用EM算法解决了截断正态分布参数的估计问题.在M步计算时,对算法提出了修正.实例计算与计算机模拟表明,修正后的算法属于广义EM算法(GEM算法)
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  相似匹配句对
     The algorithm is an extension of the two-valued cover-most algorithm proposed by M. C.
     该算法是M. C.
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     An Integrated Arithmetic of AHP and GEM
     AHP、GEM及其综合算法
短句来源
     THE GEMk ITERATIVE ALGORITHM AND THE GEOMETRY OF POLYNOMIALS
     GEM_k迭代算法和多项式几何
短句来源
     in the algorithm;
     算法简单易行。
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     (3) Some heuristicalgorithms for the ODC problem.
     (3启发式算法
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  gem algorithm
The parameters estimation is performed using a Generalized EM (GEM) algorithm.
      
A GEM algorithm is described for estimating the parameters in the model.
      
A GEM algorithm for computing LAD estimates of the parameters of nonlinear regression models is also provided and is applied in some examples.
      
Again, as in KEV adaptation, we apply GEM algorithm to find the optimal weights.
      
Theorem 2 provides the conditions under which an instance of a GEM algorithm converges.
      
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Handles the problem of parameter estimation for truncated samples from normal populations via the EM algorithm. In M step we propose a modifications of the algorithm. As shown in a real problem and computer simulation, this procedure works well and belongs to the GEM algorithm.

用EM算法解决了截断正态分布参数的估计问题.在M步计算时,对算法提出了修正.实例计算与计算机模拟表明,修正后的算法属于广义EM算法(GEM算法).

A General Expectation Maximization(GEM) training algorithm for estimating the parameters of a Probability Mapping Network(PMN) is proposed in this paper. This is an improvement of EM (Expectation Maximization)algorithm. A PMN is a four-layer, Feed-forward Neural Network (FNN) with its nodes adopting Gaussian probability density function. As a multi-category Bayes classifier, A PMN outputs classification result after inputting sample patterns. In this way, EM algorithm is generalized to deal with supervised learning...

A General Expectation Maximization(GEM) training algorithm for estimating the parameters of a Probability Mapping Network(PMN) is proposed in this paper. This is an improvement of EM (Expectation Maximization)algorithm. A PMN is a four-layer, Feed-forward Neural Network (FNN) with its nodes adopting Gaussian probability density function. As a multi-category Bayes classifier, A PMN outputs classification result after inputting sample patterns. In this way, EM algorithm is generalized to deal with supervised learning from inter-categories or unsupervised learning from intracategory. The effectiveness of the proposed network and it's GEM algorithm are verified by two experiments.

本文提出一种概率映射网络(PMN)的CEM(GeneralExpectationMaximization)训练算法,它是EM(ExpectionMaximization)算法的一种改进算法。PMN网为一个四层前馈网。它构成一个贝叶斯分类器,实现多类分类的贝叶斯判别,把输入的样本模式经网络变换为输出的分类判决,其网络节点对应于贝叶斯后验概率公式的各个变量。此PMN网络用高斯校函数作为密度函数,网络参数的训练由GEM算法实现,其学习方式为类间的监督学习和类内的非监督学习。最后的实验表明了此网络及其学习算法在分类应用中的有效性。

 
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