PROBABILITY DENSITY FUNCTION ESTIMATION BASED ON REPRESENTATIVE DATA SAMPLES

机译：基于代表数据样本的概率密度函数估计

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The relationship between the results of probability density function (PDF) estimation based on Parzen windows method and the number of observed samples is demonstrated in this paper. Based on the experimental analysis, we get that the increase of observed samples may not bring about the obvious improvement of estimated result. Then, the strategy by using the representative data samples to estimate PDF is proposed. The representative data samples are selected from the original dataset by considering Entropy- Maximization and Distance-Minimization (EMDM). Finally, the experimental results on the artificial datasets shows that the estimations of PDF by using the representative data samples can obtain the similar levels of error performance compared with the estimations on the whole dataset.

机译：本文证明了基于Parzen窗方法的概率密度函数（PDF）估计结果与观察样本数之间的关系。通过实验分析，我们发现观察样本的增加可能不会带来估计结果的明显改善。然后，提出了利用代表性数据样本进行PDF估计的策略。通过考虑熵最大化和距离最小化（EMDM）从原始数据集中选择代表性数据样本。最后，在人工数据集上的实验结果表明，与整个数据集上的估计相比，使用代表性数据样本进行的PDF估计可以获得相似的错误性能。

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来源
《Proceedings of 2011 international conference on communication technology and application》|2011年|1-5|共5页
会议地点 Beijing(CN)
作者
Jing Wang; Xiaoling Li; Jianhong Ni;
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作者单位

Modern Education Technology Center, Hebei Institute of Physical Education, Shijiazhuang, 050041, China;

Department of Foreign Languages, Hebei Institute of Physical Education, Shijiazhuang 050041, China;

Modern Education Technology Center, Hebei Institute of Physical Education, Shijiazhuang, 050041, China;

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会议组织
原文格式 PDF
正文语种 eng
中图分类通信;
关键词
Probability density function; Parzen windows; representative data sample; Entropy-Maximization; Distance-Minimization;

机译：概率密度函数; Parzen窗;代表性数据样本;熵最大;距离最小;

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PROBABILITY DENSITY FUNCTION ESTIMATION BASED ON REPRESENTATIVE DATA SAMPLES

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