Abstract
We prove in this work that, under certain conditions, an algorithm that arbitrarily merges states in the prefix tree acceptor of the sample in a consistent way, converges to the minimum DFA for the target language in the limit. This fact is used to learn automata teams, which use the different automata output by this algorithm to classify the test. Experimental results show that the use of automata teams improve the best known results for this type of algorithms. We also prove that the well known Blue-Fringe EDSM algorithm, which represents the state of art in merging states algorithms, suffices a polynomial characteristic set to converge.
Work partially supported by Spanish Ministerio de Educación y Ciencia under project TIN2007-60769.
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García, P., Vázquez de Parga, M., López, D., Ruiz, J. (2010). Learning Automata Teams. In: Sempere, J.M., García, P. (eds) Grammatical Inference: Theoretical Results and Applications. ICGI 2010. Lecture Notes in Computer Science(), vol 6339. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15488-1_6
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DOI: https://doi.org/10.1007/978-3-642-15488-1_6
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