该算法采用期望最大化(EM)聚类分析方法来识别分类及其顺序。
The algorithm USES the Expectation Maximization (EM) clustering method to identify clusters and their sequences.
这里，期望最大化算法既用来处理丢失值又用来估计模型参数。
We resort to expectation maximization (EM) algorithm for both the estimation of model parameters and the coping with missing values.
对于语者辨识，语者特定模型直接用语者的语料借助于期望值最大化算法（EM）来训练，辨识算法采用了最大事后概率法则（MAP）；
For speaker identification, Expectation Maximization Algorithm (EM) is adopted to train speaker dependent model, and afterwards recognize speaker according to Maximum a Posteriori Criterion (MAP).
在此基础上给出期望最大化算法图像恢复结果，并对恢复结果做出分析。
The deconvolved results with the compensated data and the original image data by expectation maximization (EM) algorithm for reducing the effect of out of focus light were compared respectively.
然后，通过期望-最大算法来融合前后背景的校正参数，得到最终的校正图像；
Next, with expectation-maximization algorithm, we integrate correction parameters of foreground and background to get the final corrected image.
隐马尔可夫模型参数通过期望最大化算法(EM)来估计。
以油田措施增产最大化为目标，兼顾成本、工作量等目标，建立了油田措施规划的模糊期望值模型，并给出了模型的混合智能算法。
The problem is formulated as expected value model, and the objective function is to minimize the setup costs, production costs and inventory holding costs.
以油田措施增产最大化为目标，兼顾成本、工作量等目标，建立了油田措施规划的模糊期望值模型，并给出了模型的混合智能算法。
The problem is formulated as expected value model, and the objective function is to minimize the setup costs, production costs and inventory holding costs.
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