To improve the accuracy of tracking the complex maneuver target in cluttered environment, a new state estimation algorithm based on the expectation maximization (EM) algorithm is presented.
为了提高在杂波环境下跟踪强机动目标的精度，提出了一种新的基于期望极大化(EM)算法的机动目标状态估计方法。
We resort to expectation maximization (EM) algorithm for both the estimation of model parameters and the coping with missing values.
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。
The algorithm USES the Expectation Maximization (EM) clustering method to identify clusters and their sequences.
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 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.
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。
To overcome the overflow difficulty existing in HMT model, a scaling algorithm is developed to improve expectation maximization (EM) algorithm.
为了克服HMT模型存在的计算溢出困难，采用尺度变换对EM算法进行了改进。
This paper proposes to overcome those problems by incorporating the improved RPCL (rival penalized competitive learning)algorithm and the EM(expectation maximization)algorithm into the EBF structure.
本文提出用结合改进的RPCL算法和EM算法的EBF网络结构来解决上述问题。
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).
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).
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）；
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