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In this paper, the identification of stochastic
regular languages is addressed.
For this purpose, we propose a class of algorithms which
allow
for the identification of the structure
of 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.
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