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摘要翻译：
在实时处理应用中，缺失输入向量元素的估计要求系统具有输入空间中固有的某些特征，如变量之间的相关性。计算智能技术和最大似然技术都具有这样的特点，因此对于缺失数据的归算具有重要意义。本文对缺失数据估计问题的两种方法进行了比较。第一种方法是基于当前最大似然(ML)和期望最大化的方法，第二种方法是基于Adbella和Marwala3所讨论的基于自联想神经网络和遗传算法的系统，并基于三个数据集对这两种方法的估计能力进行了比较，得出了一些结论。
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英文标题：
《Missing Data: A Comparison of Neural Network and Expectation
Maximisation Techniques》
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作者：
Fulufhelo V. Nelwamondo, Shakir Mohamed and Tshilidzi Marwala
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最新提交年份：
2007
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分类信息：
一级分类：Statistics 统计学
二级分类：Applications 应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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英文摘要：
The estimation of missing input vector elements in real time processing applications requires a system that possesses the knowledge of certain characteristics such as correlations between variables, which are inherent in the input space. Computational intelligence techniques and maximum likelihood techniques do possess such characteristics and as a result are important for imputation of missing data. This paper compares two approaches to the problem of missing data estimation. The first technique is based on the current state of the art approach to this problem, that being the use of Maximum Likelihood (ML) and Expectation Maximisation (EM. The second approach is the use of a system based on auto-associative neural networks and the Genetic Algorithm as discussed by Adbella and Marwala3. The estimation ability of both of these techniques is compared, based on three datasets and conclusions are made.
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PDF链接：
https://arxiv.org/pdf/704.3474
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