Abstract
We propose a new algorithm which allows for the identification of any stochastic deterministic regular language as well as the determination of the probabilities of the strings in the language. The algorithm builds the prefix tree acceptor from the sample set and merges systematically equivalent states. Experimentally, it proves very fast and the time needed grows only linearly with the size of the sample set.
Work partially supported under grant TIC93-0633-C02-02 from CICYT (Programa Nacional de Tecnologías de la Información y de las Comunicaciones)
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© 1994 Springer-Verlag Berlin Heidelberg
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Carrasco, R.C., Oncina, J. (1994). Learning stochastic regular grammars by means of a state merging method. In: Carrasco, R.C., Oncina, J. (eds) Grammatical Inference and Applications. ICGI 1994. Lecture Notes in Computer Science, vol 862. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-58473-0_144
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DOI: https://doi.org/10.1007/3-540-58473-0_144
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