Random DFA's can be approximately learned from sparse uniform examples
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
ICGI '98 Proceedings of the 4th International Colloquium on Grammatical Inference
Identification of DFA: data-dependent vs data-independent algorithms
ICG! '96 Proceedings of the 3rd International Colloquium on Grammatical Inference: Learning Syntax from Sentences
Learning regular languages using RFSAs
Theoretical Computer Science - Special issue: Algorithmic learning theory
Polynomial characteristic sets for DFA identification
Theoretical Computer Science
Regular inference as vertex coloring
ALT'12 Proceedings of the 23rd international conference on Algorithmic Learning Theory
Empirical Software Engineering
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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.