Abstract
In this paper, we extend the characterization of the search space of regular inference [DMV94] to sequential presentations of learning data. We propose the RPNI2 algorithm, an incremental extension of the RPNI algorithm. We study the convergence and complexities of both algorithms from a theoretical and practical point of view. These results are assessed on the Feldman task.
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References
D. Angluin. On the complexity of minimum inference of regular sets. Information and Control, pages 337–350, 1978.
D. Angluin. Inference of reversible languages. Journal of the Association for Computing Machinery, 29(3):741–765, 1982.
A. Castellanos, I. Galiano, and E. Vidal. Application of OSTIA to machine translation tasks. In Grammatical Inference and Applications, ICGI'94, number 862 in Lecture Notes in Artificial Intelligence, pages 93–105. Springer Verlag, 1994.
P. Dupont, L. Miclet, and E. Vidal. What is the search space of the regular inference ? In Grammatical Inference and Applications, ICGI'94, number 862 in Lecture Notes in Artificial Intelligence, pages 25–37. Springer Verlag, 1994.
P. Dupont. Utilisation et Apprentissage de Modèles de Langage pour la Reconnaissance de la Parole Continue. Thèse de Doctorat, Ecole Nationale Supérieure des Télécommunications, Paris, France, 1996.
K.S. Fu and T.L. Booth. Grammatical inference: Introduction and survey, part 1. IEEE Transactions on Systems, Man and Cybernetics, 5:85–111, 1975.
J.A. Feldman, G. Lakoff, A. Stolcke, and S.H. Weber. Miniature language acquisition: A touchstone for cognitive science. Technical Report TR-90-009, International Computer Science Institute, Berkeley, California, 1990.
E.M. Gold. Language identification in the limit. Information and Control, 10(5):447–474, 1967.
E.M. Gold. Complexity of automaton identification from given data. Information and Control, 37:302–320, 1978.
K.J. Lang. Random DFA's can be approximately learned from sparse uniform examples. In 5th ACM workshop on Computational Learning Theory, pages 45–52, 1992.
J. Oncina and P. GarcÃa. Inferring regular languages in polynomial update time. In N. Pérez de la Blanca, A. Sanfeliu, and E.Vidal, editors, Pattern Recognition and Image Analysis, volume 1 of Series in Machine Perception and Artificial Intelligence, pages 49–61. World Scientific, 1992.
A. Stolcke. Bayesian Learning of Probabilistic Language Models. Ph. D. dissertation, University of California, 1994.
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© 1996 Springer-Verlag Berlin Heidelberg
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Dupont, P. (1996). Incremental regular inference. In: Miclet, L., de la Higuera, C. (eds) Grammatical Interference: Learning Syntax from Sentences. ICGI 1996. Lecture Notes in Computer Science, vol 1147. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0033357
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DOI: https://doi.org/10.1007/BFb0033357
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Print ISBN: 978-3-540-61778-5
Online ISBN: 978-3-540-70678-6
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