After analysis of em algorithm, we presented a new cooperative training algorithm based on incremental learning.
本文在分析了EM算法的基础上，提出了一种新的协同训练算法。
We describe the maximum likelihood parameter estimation problem and how the em algorithm can be used for its solution.
描述最大似然参数估计问题，介绍如何用EM算法求解最大似然参数估计。
The HMM parameters were estimated by the EM algorithm.
隐马尔可夫模型参数通过期望最大化算法(EM)来估计。
EM algorithm has become one of the methods of choice for ML estimation.
EM算法是一种很有效的最大似然估计方法。
The introduction of the improved EM algorithm also reduces the risk of data underflow .
使用一种改进的EM算法降低了数据下溢的风险。
Firstly, we introduce the theory of finite mixture model and EM algorithm for maximum likelihood estimation.
首先，介绍了有限混合模型理论及应用EM算法求解极大似然估计。
We further obtain and prove the condition of the correct convergence of the EM algorithm for Gaussian mixtures.
理论分析和数值实验结果表明，高斯混合密度的EM算法的正确收敛性与混合密度的重叠度密切相关。
METHODS Based on correlation information among data, the authors analyzed data by using EM algorithm and growth curve model.
方法通过数据间的相关信息，应用EM算法和生长曲线模型进行数据分析。
After explaining the EM algorithm, this paper gives the derivation of the multi-user detection algorithm based on the EM method.
在讨论了EM算法的基本原理之后，本文详细推导了基于EM方法的多用户检测算法。
The estimation of the parameters can be easily done through EM algorithm and the order model is also easily selected by BIC criterion.
给出了该模型参数估计的EM算法，并利用BIC准则对模型进行定阶。
We calculate the ML estimation via the EM algorithm, and derive its iteration equations, which gives a closed-form solution for parameters.
我们基于EM算法来计算参数的ML估计，推导了对应的参数迭代方程，给出了参数的一个闭式解。
Especially, we give some results of the convergence of the EM algorithm for the curved exponential family under the conditions checked easily.
特别对应用广泛的曲指数族，本文在较易实际验证的条件下给出了相应的EM算法的收敛结果。
In this paper we discuss the convergence of the EM algorithm for iterative computation of maximum likelihood estimates when the observations can be viewed as incomplete data.
本文讨论EM算法的收敛性，其中EM算法是不完全数据处理中的一类重要的参数估计的迭代算法。
First we introduce the abstract form of the EM algorithm. Then we develop the EM parameter estimation procedure for one application: finding the parameters of a mixture of Gaussian densities.
首先给出em算法的抽象形式，然后研究EM参数估计方法的一个应用：求高斯混合密度的参数。
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。
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。
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。
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。
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