Learning stochastic finite automata from experts

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 1433, Issue , 1998, Pages 79-89

  de la Higuera, Colin ^a  

^a UNIVERSITÉ JEAN MONNET   (France)

Author keywords

Grammatical inference; Identification with probability one; Polynomial learning; Stochastic deterministic finite automata

Indexed keywords

COMPUTATIONAL GRAMMARS; FINITE AUTOMATA; PROBABILITY; ROBOTS;

DETERMINISTIC FINITE AUTOMATA; FREQUENCY MEASURES; GRAMMATICAL INFERENCES; HUMAN EXPERT; LEARNING PROBLEM; POLYNOMIAL IDENTIFICATION; STOCHASTIC FINITE AUTOMATON;

STOCHASTIC SYSTEMS;

EID: 22044453652     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/bfb0054066     Document Type: Conference Paper

References (19)

1. Abe, N. & Warmuth, M.K. (1992): On the Computational Complexity of Approximating Distributions by Probabilistic Automata. Machine Learning 9, pp. 205-260.
2. Angluin, D. (1982): Inference of reversible languages. Journal of the ACM 29 (3), pp. 741-765
3. Carrasco, R.C. & Oncina J. (1994): Learning Stochastic Regular Grammars by means of a State Merging Method. Proceedings of the International Colloquium on Grammatical Inference ICGI-94 (pp. 139-152). Lecture Notes in Artificial Intelligence 862, Springer-Verlag.
4. García, P. & Vidal, E. (1990): Inference of k-testable languages in the strict sense and application to syntactic pattern recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 12 (9), pp. 920-925.
5. Goan, T., Benson, N. & Etzioni, O. (1996): A Grammar Inference Algorithm for the World Wide Web. In Proceedings of the 1996 AAAI Spring Symposium on Machine Learning in Information Access (MLIA '96), Stanford, CA, AAAI Press.
6. Gold, E.M. (1967): Language identification in the limit. Inform.&Control. 10, pp. 447-474.
7. Gold, E.M. (1978): Complexity of automaton identification from given data. Information and Control 37, pp. 302-320.
8. de la Higuera, C., Oncina, J. & Vidal, E. (1996): Identification of dfa: data-dependant Vs data-independent algorithms. Proceedings of the International Colloquium on Grammatical Inference ICGI-96 (pp. 313-326). Lecture Notes in Artificial Intelligence 1147, Springer-Verlag.
9. Hoeffding, W. (1963): Probability inequalities for sums of bounded random variables. American Statistical Association Journal 58, pp. 13-30.
10. Kearns, M., Mansour, Y., Ron, D., Rubinfeld, R., Shapire, R.E. & Sellie, L. (1994): On the learnability of discrete distributions. In Proceedings of the 24th Annual ACM Symp. on Theory of Computing.
11. Lari, K. & Young, S.J. (1990): The estimation of stochastic context free grammars using the inside outside algorithm, Comput. Speech. Language 4, pp 35-56.
12. Lucas, S., Vidal, E., Amiri, A., Hanlon, S. & Amengual, J.C. (1994): A comparison of syntactic and statistical techniques for off-line OCR. Proceedings of the International Colloquium on Grammatical Inference ICGI-94 (pp. 168-179). Lecture Notes in Artificial Intelligence 862, Springer-Verlag.
13. Ney, H. (1995): Stochastic grammars and Pattern Recognition. In Speech Recognition and Understanding, edited by P. Laface and R. de Mori, Springer-Verlag, pp. 45-360.
14. Oncina, J. & García, P. (1992): Inferring regular languages in polynomial time. In Pattern Recognition and Image Analysis, World Scientific.
15. Rabiner, L. &Juang, B. H. (1993): Fundamentals of Speech Recognition. Prentice-Hall.
16. Ron, D., Singer, Y. & Tishby, N. (1995): On the Learnability and Usage of Acyclic Probabilistic Finite Automata. Proceedings of COLT 1995, pp 31-40.
17. Rulot, H. & Vidal, E. (1987): Modelling (Sub)string-Length-Based Constraints through a grammatical Inference Method. In Pattern Recognition: Theory and Applications. Eds: Devijver and Kittler, pp.451-459, Springer Verlag.
18. Sakakibara, Y. (1997): Recent Advances of grammatical inference. Theoretical Computer Science 185, pp. 1545.
19. Stolcke, A. & Omohundro, S. (1994): Inducing Probabilistic Grammars by Bayesian Model Merging. In Proceedings of the International Colloquium on Grammatical Inference ICGI-94 (pp. 106-118). Lecture Notes in Artificial Intelligence 862, Springer-Verlag.