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Abstract: Efficient learning of DFA is a challenging research problem in grammatical inference. It is known that both exact and approximate (in the PAC sense) identifiability of DFA is hard. Pitt, in his seminal paper posed the following open research problem: "Are DFAPAC-identifiable if examples are drawn from the uniform distribution, or some other known simple distribution?" [25]. We demonstrate that the class of simple DFA (i.e., DFA whose canonical representations have logarithmic Kolmogorov... (Update)
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BibTeX entry: (Update)
B. Parekh and V. Honavar. Learning DFA from simple examples. volume 1316 of LNAI, pages 116--131, Berlin, 1997. Springer. http://citeseer.ist.psu.edu/article/parekh01learning.html More
@artilcle{ parekh97learning,
author = "Rajesh Parekh and Vasant Honavar",
title = "DFA Learning from simple examples",
journal = "Machine Learning",
volume = "44",
pages = "9-35",
year = "2001",
url = "citeseer.ist.psu.edu/article/parekh01learning.html" }
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