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Numerical Estimation of Information Theoretic Measures for Large Data Sets.

机译：大数据集信息理论测度的数值估计。

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摘要

A problem that has plagued the tracking community for decades has been the lack of a single metric to assess the overall performance of tracking systems. The authors' prior research has identified total conditional entropy as a useful measure to assess the overall performance of multi-target trackers and classifiers. The measure can be used to evaluate any system that can be formulated as an assignment algorithm that maps N classes of objects to M labels. The assignments from the decision system are compared to a truth data set to generate the total conditional entropy. This report focuses on work to generate error estimates so that the statistical significance of test results can be determined. Derivations of Wolpert and Wolf provide exact equations for calculating the first- and second-order statistics for the three fundamental entropy measures from sample data. The authors have restructured Wolpert and Wolf's equations into computer code that produces stable numerical results up to sample sizes in the billions. This code is provided as a MATLAB software package available from MIT Lincoln Laboratory.

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作者
E. K. Kao M. B. Hurley;
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年度 2013
页码 1-86
总页数 86
原文格式 PDF
正文语种 eng
中图分类工业技术;
关键词
Computer programs; Covariance; Covariance equations; Data normalization; Entropy; Error bounds; Error estimation; First order statistics; Information theory; Multi-target classifiers; Multi-target trackers; Normalizing(Statistics); Numerical estimates; Order statistics; Second order statistics; Sensor data; Statistical errors; Target classification; Total conditional entropy; Tracking; Tracking algorithms; Truth data;

机译：计算机程序;协方差;协方差方程;数据归一化;熵;误差界;误差估计;一阶统计;信息论;多目标分类器;多目标跟踪器;归一化（统计）;数值估计;订单统计;二阶统计;传感器数据;统计误差;目标分类;总条件熵;跟踪;跟踪算法;真实数据;

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Numerical Estimation of Information Theoretic Measures for Large Data Sets.

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