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Universal automata and NFA learning

机译:通用自动机和NFA学习

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The aim of this paper is to develop a new algorithm that, with a complete sample as input, identifies the family of regular languages by means of nondeterministic finite automata. It is a state-merging algorithm. One of its main features is that the convergence (which is proved) is achieved independently from the order in which the states are merged, that is, the merging of states may be done "randomly".
机译:本文的目的是开发一种新算法,该算法以完整的样本作为输入,通过非确定性有限自动机识别常规语言族。它是一种状态合并算法。它的主要特征之一是,收敛(已证明)与状态合并的顺序无关,也就是说,状态合并可以“随机”完成。

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