Notes on “Reducing Algorithm Complexity for Computing an Aggregate Uncertainty Measure”
Notes on “Reducing Algorithm Complexity for Computing an Aggregate Uncertainty Measure”
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DOI：
10.1109/tsmca.2009.2030962
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发表时间：
2010
期刊：
影响因子：
--
通讯作者：
V. Huynh;Y. Nakamori
中科院分区：
文献类型：
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作者：
V. Huynh;Y. Nakamori
在最近的一篇论文中，刘提出了有条件地降低Meyerowitz-Richman-Walker算法的计算复杂度的所谓F-算法，该算法用于计算Dempster-Shafer证据理论中的聚合不确定度，并举例说明其在目标识别的实际场景中的应用。在这篇通信中，我们将指出几个技术错误，其中一些错误导致了刘的论文中的一些不准确或不完整的陈述，并对这些错误进行了纠正，并得出了一些进一步的改进和结果。
In a recent paper, Liu have proposed the so-called F -algorithm which conditionally reduces the computational complexity of the Meyerowitz-Richman-Walker algorithm for the computation of the aggregate-uncertainty measure in the Dempster-Shafer theory of evidence, along with an illustration of its application in a practical scenario of target identification. In this correspondence, we will point out several technical mistakes, which some of them lead to some inexact or incomplete statements in the paper of Liu The corrections of these mistakes will be made, and some further improvement and results will be derived.