Abstract
A new version of the RPNI algorithm, called RPNI2, is presented. The main difference between them is the capability of the new one to extend the training set during the inference process. The effect of this new feature is specially notorious in the inference of languages generated from regular expressions and Non-deterministic Finite Automata (NFA). A first experimental comparison is done between RPNI2 and DeLeTe2, other algorithm that behaves well with the same sort of training data.
Keywords
- Regular Expression
- Target Language
- Regular Language
- Inclusion Relation
- Grammatical Inference
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Work partially supported by Spanish CICYT under TIC2003-09319-C03-02
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García, P., Ruiz, J., Cano, A., Alvarez, G. (2005). Inference Improvement by Enlarging the Training Set While Learning DFAs. In: Sanfeliu, A., Cortés, M.L. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2005. Lecture Notes in Computer Science, vol 3773. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11578079_7
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DOI: https://doi.org/10.1007/11578079_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29850-2
Online ISBN: 978-3-540-32242-9
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