Abstract
Inference of RFSAs has been recently presented [1] as an alternative to inference of DFAs if the target language has been obtained by a random generation of NFAs. We propose in this paper the algorithm RPNI2, which is a variation of the previous RPNI, that also outputs DFAs as hypothesis. The experiments done using the same data as in [1] show that RPNI2 has an error rate very similar to the rate obtained in the inference of RFSAs, but the size of the hypothesis is substantially smaller.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Denis, F., Lemay, A., Terlutte, A.: Learning regular languages using rfsas. Theoretical Computer Science 313, 267–294 (2004)
Oncina, J., García, P.: Inferring regular languages in polynomial updated time. In: Pattern Recognition and Image Analysys (1992)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
García, P., Ruiz, J., Cano, A., Alvarez, G. (2006). Is Learning RFSAs Better Than Learning DFAs?. In: Farré, J., Litovsky, I., Schmitz, S. (eds) Implementation and Application of Automata. CIAA 2005. Lecture Notes in Computer Science, vol 3845. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11605157_30
Download citation
DOI: https://doi.org/10.1007/11605157_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-31023-5
Online ISBN: 978-3-540-33097-4
eBook Packages: Computer ScienceComputer Science (R0)