Stochastic grammatical inference with multinomial tests

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 2484, Issue , 2002, Pages 149-160

  Kermorvant, Christopher ^a   Dupont, Pierre ^b  

^a UNIVERSITÉ JEAN MONNET   (France)

^b UNIVERSITÉ CATHOLIQUE DE LOUVAIN   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTATIONAL GRAMMARS; MERGING; STOCHASTIC SYSTEMS;

ARTIFICIAL DATA; ASYMPTOTIC RESULTS; BEST-FIRST STRATEGY; GRAMMATICAL INFERENCE ALGORITHM; GRAMMATICAL INFERENCES; SMALL SAMPLE CASE; STATISTICAL EVIDENCE; STATISTICAL FRAMEWORK;

INFERENCE ENGINES;

EID: 84943230024     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/3-540-45790-9_12     Document Type: Conference Paper

References (16)

1. J. Baglivo, D. Olivier, and M. Pagano. Methods for analysis of contingency tables with large an small cell counts. Journal of the American Statistical Association, 83(404):1006-1013, 1988.
2. R. C. Carrasco and J. Oncina. Learning stochastic regular grammars by means of a state merging method. In Proc. Int. Coll. on Grammatical Inference, volume 862 of Lecture Notes in Artificial Intelligence, pages 139-152. Springer Verlag, 1994.
3. R. C. Carrasco and J. Oncina. Learning deterministic regular grammars from stochastic samples in polynomial time. RAIRO (Theoretical Informatics and Applications), 33(1):1-20, 1999.
4. N. Cressie and T. R. C. Read. Multinomial goodness-of-fit tests. Journal of the Royal Statistical Society Series B, 46:440-464, 1984.
5. C. de la Higuera, J. Oncina, and E. Vidal. Identification of DFA: data-dependent vs data-independent algorithms. In Laurent Miclet and Colin de la Higuera, editors, Proceedings of the Third International Colloquium on Grammatical Inference (ICGI-96): Learning Syntax from Sentences, volume 1147 of LNAI, pages 313-325, Berlin, September 25-27 1996. Springer.
6. W. Hoeffding. Probability inequalities for sums of bounded random variables. Journal of the American Statistical Association, 58(301):13-30, March 1963.
7. Kevin J. Lang, Barak A. Pearlmutter, and Rodney A. Price. Results of the Abbadingo One DFA learning competition and a new evidence-driven state merging algorithm. In Springer-Verlag, editor, Proc. Int. Coll. on Grammatical Inference, volume 1433 of LNAI, pages 1-12, 1998.
8. D. L. March. Exact probability for r × c contingency tables. Communications of the ACM, 15(11):991-992, November 1972.
9. C. Mehta and N. R. Patel. A network algorithm for performing fisher's exact test in r×c contingency tables. Journal of the American Statistical Association, 78(382):427-434, 1983.
10. M. Pagano and K. Taylor Halvorsen. An algorithm for finding the exact significance levels of r ×c contingency tables. Journal of the American Statistical Association, 76(376):931-934, 1981.
11. K. Pearson. On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. Philosophy Magazine, 50:157-172, 1900.
12. A. S. Reber. Implicit learning of artificial grammars. Journal of verbal learning and verbal behaviour, 6:855-863, 1967.
13. D. Ron, Y. Singer, and N. Tishby. On the learnability and usage of acyclic probabilistic automata. In Proceedings of the Eighth Annual Conference on Computational Learning Theory, pages 31-40, Santa Cruz, CA, 1995. ACM Press.
14. F. Thollard and P. Dupont. Probabilistic DFA inference using Kullback-Leibler divergence and minimality. In Proc. Int. Conf. on Machine Learning, pages 975-982. Morgan Kaufmann, San Francisco, CA, 2000.
15. S. S. Wilks. The large-sample distribution of the likelihood ratio for testing composite hypotheses. Annals of Mathematical Statistics, 9:60-62, 1938.
16. M. Young-Lai and F. WM. Tompa. Stochastic grammatical inference of text database structure. Machine Learning, 40:111-137, 2000.