Choquet积分与Sugeno积分作为模式识别聚合算子的比较

Gabriela E. Martinez, O. Mendoza, J. R. Castro, Antonio Rodríguez Díaz, P. Melin, O. Castillo
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引用次数: 9

摘要

本文对Choquet积分和Sugeno积分进行了比较。所提出的方法使Choquet和Sugeno积分的计算能够结合具有一定程度不确定性的多个信息源。将该方法用于人脸识别的模块神经网络的模块输出进行组合,并进行比较。在本文中,重点是使用度量作为输入的聚合算子,特别是Choquet和Sugeno积分。Choquet积分的识别结果优于Sugeno积分的识别结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparison between Choquet and Sugeno integrals as aggregation operators for pattern recognition
In this paper, a comparison of the Choquet and Sugeno integrals is presented. The proposed methods enable the calculation of the Choquet and Sugeno integrals to combine multiple source of information with a degree of uncertainty. The methods are used to combine the modules output of a modular neural network for face recognition and a comparison is performed. In this paper, the focus is on aggregation operators that use measures as inputs, in particular the Choquet and Sugeno integrals. Recognition results with the Choquet integral are better or comparable to results produced by Sugeno integral.
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