Inference improvement by enlarging the training set while learning DFAs

Pedro García; José Ruiz; Antonio Cano; Gloria Alvarez

doi:10.1007/11578079_7

Inference improvement by enlarging the training set while learning DFAs

Pedro García, José Ruiz, Antonio Cano, Gloria Alvarez

Polytechnic University of Valencia

Producción: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

5 Citas (Scopus)

Resumen

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.¹

Idioma original	Inglés
Título de la publicación alojada	Progress in Pattern Recognition, Image Analysis and Applications - 10th Iberoamerican Congress on Pattern Recognition, CIARP 2005, Proceedings
Editorial	Springer Verlag
Páginas	59-70
Número de páginas	12
ISBN (versión impresa)	3540298509, 9783540298502
DOI	https://doi.org/10.1007/11578079_7
Estado	Publicada - 2005
Evento	10th Iberoamerican Congress on Pattern Recognition, CIARP 2005 - Havana, Cuba Duración: 15 nov. 2005 → 18 nov. 2005

Serie de la publicación

Nombre	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen	3773 LNCS
ISSN (versión impresa)	0302-9743
ISSN (versión digital)	1611-3349

Conferencia

Conferencia	10th Iberoamerican Congress on Pattern Recognition, CIARP 2005
País/Territorio	Cuba
Ciudad	Havana
Período	15/11/05 → 18/11/05

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10.1007/11578079_7

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García, P., Ruiz, J., Cano, A., & Alvarez, G. (2005). Inference improvement by enlarging the training set while learning DFAs. En Progress in Pattern Recognition, Image Analysis and Applications - 10th Iberoamerican Congress on Pattern Recognition, CIARP 2005, Proceedings (pp. 59-70). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3773 LNCS). Springer Verlag. https://doi.org/10.1007/11578079_7

García, Pedro ; Ruiz, José ; Cano, Antonio et al. / Inference improvement by enlarging the training set while learning DFAs. Progress in Pattern Recognition, Image Analysis and Applications - 10th Iberoamerican Congress on Pattern Recognition, CIARP 2005, Proceedings. Springer Verlag, 2005. pp. 59-70 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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.1",

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García, P, Ruiz, J, Cano, A & Alvarez, G 2005, Inference improvement by enlarging the training set while learning DFAs. En Progress in Pattern Recognition, Image Analysis and Applications - 10th Iberoamerican Congress on Pattern Recognition, CIARP 2005, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3773 LNCS, Springer Verlag, pp. 59-70, 10th Iberoamerican Congress on Pattern Recognition, CIARP 2005, Havana, Cuba, 15/11/05. https://doi.org/10.1007/11578079_7

Inference improvement by enlarging the training set while learning DFAs. / García, Pedro; Ruiz, José; Cano, Antonio et al.
Progress in Pattern Recognition, Image Analysis and Applications - 10th Iberoamerican Congress on Pattern Recognition, CIARP 2005, Proceedings. Springer Verlag, 2005. p. 59-70 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3773 LNCS).

Producción: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

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N2 - 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.1

AB - 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.1

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García P, Ruiz J, Cano A, Alvarez G. Inference improvement by enlarging the training set while learning DFAs. En Progress in Pattern Recognition, Image Analysis and Applications - 10th Iberoamerican Congress on Pattern Recognition, CIARP 2005, Proceedings. Springer Verlag. 2005. p. 59-70. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/11578079_7

Inference improvement by enlarging the training set while learning DFAs

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