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 gem算法 的翻译结果: 查询用时：0.023秒
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 gem算法
 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）． 短句来源
 “gem算法”译为未确定词的双语例句
 They also defined Generalised EM algorithm(GEM ),which includes EM as a speial case ,and can be more computaionally efficient and guarantee the quality of EM algorithm. It is a general-purpose algorithm for maximum likelihood estimation in wide variety of situations best described as incomplete-data problem. 另外，他们还定义了GEM算法，把EM算法视为GEM算法的特例．GEM算法在计算上更有效，且保持了对数似然单调不减的特性。 短句来源 In this way, EM algorithm is generalized to deal with supervised learning from inter-categories or unsupervised learning from intracategory. 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。 短句来源
 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. 短句来源 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 Agrawal's Apriori algorithm. GenApriori算法是R. 短句来源

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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. 更多
 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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