A new general method for inference of regular languages using nondeterministic automata as output has recently been developed and proved to converge. The aim of this paper is to describe and analyze the behavior of two implementations of that method and to compare it with two well known algorithms for the same task. A complete set of experiments has been carried out and the results of the new algorithms improve the existing ones both in recognition rates as in sizes of the output automata.
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