部分定义函数确定集的信息论挖掘

D. Simovici, D. Pletea, R. Vetro
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引用次数: 7

摘要

本文描述了一种确定决定离散偏函数值的最小变量集的算法。该算法基于分区熵的概念,能够得到最优解。引入一个限制因素来限制搜索,从而提供减少运行时间的选项。实验结果表明,该算法对于多达24个变量的函数是有效的。对不同大小的部分函数,考察了限制因子对算法最优性的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Information-Theoretical Mining of Determining Sets for Partially Defined Functions
This paper describes an algorithm that determines the minimal sets of variables that determine the values of a discrete partial function. The algorithm is based on the notion of entropy of a partition and is able to achieve an optimal solution. A limiting factor is introduced to restrict the search, thereby providing the option to reduce running time. Experimental results are provided that demonstrate the efficiency of the algorithm for functions with up to 24 variables. The effect of the limiting factor on the optimality of the algorithm for different sizes of partial functions is also examined.
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