Abstract
In this article we study the inference of commutative regular languages. We first show that commutative regular languages are not inferable from positive samples, and then we study the possible improvement of inference from positive and negative samples. We propose a polynomial algorithm to infer commutative regular languages from positive and negative samples, and we show, from experimental results, that far from being a theoretical algorithm, it produces very high recognition rates in comparison with classical inference algorithms.
Work partially supported by Ministerio de Educación y Ciencia under project TIN2007-60760.
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Cano Gómez, A., Álvarez, G.I. (2008). Learning Commutative Regular Languages. In: Clark, A., Coste, F., Miclet, L. (eds) Grammatical Inference: Algorithms and Applications. ICGI 2008. Lecture Notes in Computer Science(), vol 5278. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88009-7_6
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DOI: https://doi.org/10.1007/978-3-540-88009-7_6
Publisher Name: Springer, Berlin, Heidelberg
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