Unambiguous automata inference by means of state-merging methods

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

Volumn 2837, Issue , 2003, Pages 60-71

  Coste, François ^a   Fredouille, Daniel ^a  

^a CAMPUS DE BEAULIEU   (France)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DATA ACQUISITION; HEURISTIC METHODS; MATHEMATICAL MODELS; MATHEMATICAL OPERATORS; POLYNOMIALS; PROBLEM SOLVING; QUERY LANGUAGES; ARTIFICIAL INTELLIGENCE; AUTOMATA THEORY; FINITE AUTOMATA; LEARNING SYSTEMS; PIPELINE PROCESSING SYSTEMS;

AUTOMATA INFERENCE; NONDETERMINISTIC AUTOMATA (NFA); UNAMBIGUOUS AUTOMATA (UFA); CLASSICAL PROBLEMS; DETERMINISTIC AUTOMATA; NONDETERMINISTIC AUTOMATA; SEARCH SPACES; STATE-MERGING;

AUTOMATA THEORY; INFERENCE ENGINES;

EID: 9444281493     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-39857-8_8     Document Type: Conference Paper

References (17)

1. [Abb98] Abbadingo one, 1998. http://abbadingo.cs.unm.edu/.
2. [AS95] R. Alquézar and A. Sanfeliu. Incremental grammatical inference from positive and negative data using unbiased finite state automata. In Shape, Structure and Pattern Recognition, Proc. Int. Workshop on Structural and Syntactic Pattern Recognition, SSPR'94, Nahariya (Israel), pages 291-300, 1995.
3. [CF00] F. Coste and D. Fredouille. Efficient ambiguity detection in C-NFA, a step toward inference of non deterministic automata. Grammatical Inference: Algorithms and Applications, ICGI'00, pages 25-38, 2000.
4. [CF03] F. Coste and D. Fredouille. What is the search space for the inference of nondeterministic, unambiguous and deterministic automata? Technical report, IRISA, to appear, download: http://www.irisa.fr/prive/dfredoui/down/ report.ps.gz, 2003.
5. [Cos99] F. Coste. State merging inference of finite state classifiers. Technical Report INRIA/RR-3695, IRISA, September 1999.
6. [Die98] Thomas G. Dietterich. Approximate statistical test for comparing supervised classification learning algorithms. Neural Computation, 10(7):1895-1923, 1998.
7. [DLT00] F. Denis, A. Lemay, and A. Terlutte. Learning regular languages using non deterministic finite automate. Grammatical Inference: Algorithms and Applications, ICGI'00, 2000.
8. [DLT01] F. Denis, A. Lemay, and A. Terlutte. Learning regular languages using RFSA. In Proceedings of the 12th International Conference on Algorithmic Learning Theory, ALT'01, pages 348-363, 2001.
9. [DMV94] P. Dupont, L. Miclet, and E. Vidal. What is the search space of the regular inference? Grammatical inference and Applications, ICGI'94, pages 25-37, 1994. Springer Verlag.
10. [DP90] B. Davey and A. Priesley. Introduction to lattices and order. Cambridge mathematical textbooks, 1990.
11. [Dup96] P. Dupont. Utilisation et apprentissage de modèles de langages pour la reconnaissance de la parole continue. PhD thesis, Ecole Nationale Supérieure des Télécommunications, 1996.
12. [Gol78] E. M. Gold. Complexity of automaton identification from given data. Information and Control, 37:302-320, 1978.
13. [Hig97] C. Higuera (de la). Characteristic sets for polynomial grammatical inference. Machine Learning, 27:125-138, 1997.
14. [Lan92] K. J. Lang. Random dfa's can be approximately learned from sparse uniform examples. 5th ACM workshop on Computation Learning Theorie, pages 45 -52, 1992.
15. [LPP98] K. J. Lang, B. A. Pearlmutter, and R. A. Price. Results of the abbadingo one DFA learning competition and a new evidence-driven state merging algorithm. Lecture Notes in Computer Science, 1433:1-12, 1998.
16. [OG92] J. Oncina and P. García. Inferring regular languages in polynomial update time. Pattern Recognition and Image Analysis, pages 49-61, 1992.
17. [PW89] L. Pitt and M. Warmuth. The minimum consistent DFA problem cannot be approximated within any polynomial. In 21st ACM Symposium on Theory of Computing, pages 421-444, 1989.