Learning Probabilistic Subsequential Transducers from Positive Data

Hasan Ibne Akram, Colin de la Higuera

Abstract

In this paper we present a novel algorithm for learning probabilistic subsequential transducers from a randomly drawn sample. We formalize the properties of the training data that are sufficient conditions for the learning algorithm to infer the correct machine. Finally, we report experimental evidences to backup the correctness of our proposed algorithm.

References

  1. Akram, H. I., de la Higuera, C., and Eckert, C. (2012). Actively learning probabilistic subsequential transducers. In Proceedings of ICGI, volume 21 of JMLR:Workshop and Conference Proceedings. MIT Press.
  2. Allauzen, C., Riley, M., Schalkwyk, J., Skut, W., and Mohri, M. (2007). Openfst: A general and efficient weighted finite-state transducer library. In Proceedings of CIAA, volume 4783 of Lecture Notes in Computer Science, pages 11-23. Springer.
  3. Carrasco, R. C. and Oncina, J. (1994). Learning stochastic regular grammars by means of a state merging method. In Proceedings of ICGI, volume 862 of Lecture Notes in Computer Science, pages 139-152. Springer.
  4. Carrasco, R. C. and Oncina, J. (1999). Learning deterministic regular grammars from stochastic samples in polynomial time. Rairo - Theoratical Informatics and Applications, 33(1):1-20.
  5. Castellanos, A., Vidal, E., Varó, M. A., and Oncina, J. (1998). Language understanding and subsequential transducer learning. Computer Speech & Language, 12(3):193-228.
  6. de la Higuera, C. (2010). Grammatical Inference: Learning Automata and Grammars. Cambridge University Press.
  7. Durbin, R., Eddy, S. R., Krogh, A., and Mitchison, G. (1998). Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. Cambridge University Press.
  8. Feldman, J. A., Lakoff, G., Stolcke, A., and Weber, S. H. (1990). Miniature language acquisition: A touchstone for cognitive science. Technical Report TR-90- 009, International Computer Science Institute, Berkeley CA.
  9. Hoeffding, W. (1963). Probability inequalities for sums of bounded random variables. American Statistical Association Journal, 58:13-30.
  10. Levenshtein, V. I. (1966). Binary codes capable of correcting deletions, insertions and reversals. Soviet Physics Doklady, 10:707.
  11. Oncina, J. and García, P. (1991). Inductive learning of subsequential functions. Technical report, Univ. Polit'ecnica de Valencia.
  12. Oncina, J., García, P., and Vidal, E. (1993). Learning subsequential transducers for pattern recognition interpretation tasks. IEEE Trans. Pattern Anal. Mach. Intell., 15(5):448-458.
  13. Rabiner, L. R. (1990). Readings in speech recognition. chapter A tutorial on hidden Markov models and selected applications in speech recognition, pages 267- 296. Morgan Kaufmann Publishers Inc.
  14. Thollard, F., Dupont, P., and de la Higuera, C. (2000). Probabilistic dfa inference using Kullback-Leibler divergence and minimality. In Proceedings of ICML, pages 975-982. Morgan Kaufmann.
  15. a : x(250) b : y(870)
Download


Paper Citation


in Harvard Style

Ibne Akram H. and de la Higuera C. (2013). Learning Probabilistic Subsequential Transducers from Positive Data . In Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 1: LAFLang, (ICAART 2013) ISBN 978-989-8565-38-9, pages 479-486. DOI: 10.5220/0004359904790486


in Bibtex Style

@conference{laflang13,
author={Hasan Ibne Akram and Colin de la Higuera},
title={Learning Probabilistic Subsequential Transducers from Positive Data},
booktitle={Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 1: LAFLang, (ICAART 2013)},
year={2013},
pages={479-486},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004359904790486},
isbn={978-989-8565-38-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 1: LAFLang, (ICAART 2013)
TI - Learning Probabilistic Subsequential Transducers from Positive Data
SN - 978-989-8565-38-9
AU - Ibne Akram H.
AU - de la Higuera C.
PY - 2013
SP - 479
EP - 486
DO - 10.5220/0004359904790486