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Published online by Cambridge University Press: 15 August 2002
In this paper, the identification of stochasticregularlanguages is addressed.For this purpose, we propose a class of algorithms which allowfor the identification of the structureof the minimal stochastic automaton generating the language. It is shown that the time needed grows only linearly with the size of the sample set and a measure of the complexity of the task is provided.Experimentally, our implementation proves very fast for application purposes.