Learning DFA from simple examples

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

Volumn 1316, Issue , 1997, Pages 116-131

  Parekh, Rajesh ^a   Honavar, Vasant ^a  

^a Iowa State University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; TEACHING;

CLASS OF DISTRIBUTIONS; EFFICIENT LEARNING; EXACT LEARNING; HIGH PROBABILITY; NUMBER OF STATE; RESEARCH QUESTIONS; TARGET CONCEPT; UNIFORM DISTRIBUTION;

LEARNING ALGORITHMS;

EID: 84958043718     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-63577-7_39     Document Type: Conference Paper

References (21)

1. Angluin, D. (1981). A Note on the Number of Queries Needed to Identify Regular Languages. Information and Control, 51, 76-87.
2. Angluin, D. (1987). Learning Regular Sets from Queries and Counterexamples. Information and Computation, 75, 87-106.
3. Chomsky, N. (1956). Three Models for the Description of Language. PGIT, 2(3), 113-124.
4. Denis, F., D'Halluin, C., & Gilleron, R. (1996). PAC Learning with Simple Examples. STACS'96. Proceedings of the 13 'h Annual Symposium on the Theoretical Aspects of Computer Science, 231-242.
5. Dupont, P. (1996a). Incremental Regular Inference. Pages 222-237 of: Miclet, L., & Higuera, C. (eds), Proceedings of the Third ICGI-96, Lecture Notes in Artificial Intelligence 1147. Montpellier, France: Springer.
6. Dupont, P. (1996b). Utilisation et Apprentissage de ModUles de Language pour la Reconnaissance de la Parole Continue. Ph.D. thesis, Ecole Normale Supérieure des Télécommunications, Paris, France.
7. Dupont, P., Miclet, L., & Vidal, E. (September 1994). What is the Search Space of the Regular Inference? Pages 25-37 of: Proceedings of the Second International Colloquium on Grammatical Inference (ICGI'94).
8. Gold, E. M. (1978). Complexity of Automaton Identification from Given Data. Information and Control, 37(3), 302-320.
9. Hopcroft, J., & Ullman, J. (1979). Introduction to Automata Theory, Languages, and Computation. Addison Wesley.
10. Kearns, M., & Valiant, L. G. (May 1989). Cryptographic Limitations on Learning Boolean Formulae and Finite Automata. Pages 433-444 of: Proceedings of the 21" Annual ACM Symposium on Theory of Computing. ACM, New York.
11. Lang, K. J. (1992). Random DFA's can be approximately learned from sparse uniform sample. Pages 45-52 of: Proceedings of the 5th ACM workshop on Computational Learning Theory.
12. Li, M., & Vitányi, P. (1991). Learning Simple Concepts under Simple Distributions. SlAM Journal of Computing, 20, 911-935.
13. Li, M., & Vitányi, P. (1997). An Introduction to Kolmogorov Complexity and its Applications, 2 nd edition. New York: Springer Verlag.
14. Oncina, J., & Garcia, P. (1992). Inferring Regular Languages in Polynomial Update Time. Pages 49-61 of: Pérez, N. et al (ed), Pattern Recognition and linage Analysis. World Scientific.
15. Pao, T., & Cart, J. (1978). A Solution of the Syntactic Induction-Inference Problem for Regular Languages. Computer Languages, 3, 53-64.
16. Parekh, R.G., & Honavar, V. G. (August 1993). Efficient Learning of Regular Languages using Teacher Supplied Positive Examples and Learner Generated Queries. Pages 195-203 of: Proceedings of the Fifth UNB Conference on AL
17. Pitt, L. (1989). Inductive Inference, DFAs and Computational Complexity. Pages 18-44 of: Analogical and Inductive Inference, Lecture Notes in Artificial Intelligence 397. Springer-Verlag.
18. Pitt, L., & Warmuth, M. K. (1988). Reductions among prediction problems: on the difficulty of predicting automata. Pages 60-69 of: Proceedings of the 3 rd IEEE. Conference on Structure in Complexity Theory.
19. Pitt, L., & Warmuth, M. K. (1989). The minimum consistency DFA problem cannot be approximated within any polynomial. Pages 421-432 of: Proceedings of the 21 st ACM Symposium on the Theory of Computing. ACM.
20. Trakhtenbrot, B., & Barzdin, Ya. (1973). Finite Automata: Behavior and Synthesis. Amsterdam: North Holland Publishing Company.
21. Valiant, L. (1984). A Theory of the Learnable. Communications of the ACM, 27, 1134-1142.