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Analysis of Membership Functions for Voronoi-Based Classification

机译:基于Voronoi的分类的隶属度函数分析

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This paper addresses the problem of membership function selection for zoning-based classification. Different types of membership functions are considered based on abstract-level, ranked-level and measurement-level models and their effectiveness is estimated under different Voronoi-based zoning methods. The experimental tests, carried out in the field of hand-written numeral recognition, show that the best results are obtained when measurement-level models based on exponential models are used as membership functions.
机译:本文针对基于分区的分类解决隶属函数选择的问题。基于抽象级别,等级级别和度量级别的模型考虑了不同类型的隶属函数,并在基于Voronoi的不同分区方法下评估了它们的有效性。在手写数字识别领域进行的实验测试表明,将基于指数模型的测量级模型用作隶属函数时,可获得最佳结果。

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