该方法采用核密度估计模型来构造近似密度函数，利用爬山策略来提取聚类模式。
This method USES kernel density estimation model to construct the approximate density function, and takes hill climbing strategy to extract clustering patterns.
正态混合模型还可以用来对那些不能用标准的参数分布族来拟和的总体进行密度估计或近似。
The normal mixed distribution model can be used to get probability density or to simulate population which can not be fitted by standard parameters distribution classes.
在充分研究现有运动目标检测算法的基础上，提出了一种新的非参数核密度估计背景模型。
A new background model of non-parameter kernel density estimate was presented on the basis of abundant study on algorithms of moving object detection.
但是这种方法也有不足之处，就在于它对模型有一些弱的假定点估计依赖于误差因子与模型参数的假定，密度估计依赖于误差因子特征函数的假定。
The disadvantages were that this method was based on assumptions on the model: point estimation based on parametric assumption and some properties of error components.
核密度估计方法从数据样本本身出发研究数据分布特征，不利用有关数据分布的先验知识，避免了模型估计和参数估计的主观影响。
The kernel estimation method analyzes the data distribution by not using the prior knowledge of data distribution. This method avoids the impaction of model and parameters estimation.
核密度估计方法从数据样本本身出发研究数据分布特征，不利用有关数据分布的先验知识，避免了模型估计和参数估计的主观影响。
The kernel estimation method analyzes the data distribution by not using the prior knowledge of data distribution. This method avoids the impaction of model and parameters estimation.
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