Learning efficiency of very simple grammars from positive data

Theoretical Computer Science

Volumn 410, Issue 19, 2009, Pages 1807-1825

  Yoshinaka, Ryo ^a  

^a HOKKAIDO UNIVERSITY   (Japan)

Author keywords

Grammatical inference; Identification in the limit; Learning efficiency; Very simple grammars

Indexed keywords

COMPUTATIONAL GRAMMARS; EDUCATION; LEARNING SYSTEMS;

GRAMMATICAL INFERENCE; IDENTIFICATION IN THE LIMIT; LEARNING EFFICIENCY; POLYNOMIAL-TIME; POSITIVE DATUM; VERY SIMPLE GRAMMARS;

LEARNING ALGORITHMS;

EID: 62749186024     PISSN: 03043975     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.tcs.2009.01.012     Document Type: Article

References (18)

1. Angluin D. Finding patterns common to a set of strings. Journal of Computer and System Sciences 21 1 (1980) 46-62
2. Angluin D. Inference of reversible languages. Journal of the Association for Computing Machinery 29 3 (1982) 741-765
3. Clark A., and Eyraud R. Polynomial identification in the limit of context-free substitutable languages. Journal of Machine Learning Research 8 (2007) 1725-1745
4. Higuchi K., Tomita E., and Wakatsuki M. A polynomial-time algorithm for checking the inclusion for strict real-time deterministic restricted one-counter automata. IEICE transactions on information and systems E 78-D 4 (1995) 305-313
5. de la Higuera C. Characteristic sets for polynomial grammatical inference. Machine Learning 27 (1997) 125-138
6. Lange S., and Wiehagen R. Polynomial-time inference of arbitrary pattern languages. New Generation Computing 8 (1991) 361-370
7. Linna M. Two decidability results for deterministic pushdown automata. Journal of Computer and System Science 18 (1979) 92-107
8. Pitt L. Inductive inference, DFAs, and computational complexity. Proceedings of 2nd Workshop on Analogical and Inductive Inference. Lecture Notes in Computer Science vol. 397 (1989) 18-44
9. Takada Y. Grammatical inference for even linear languages based on control sets. Information Processing Letters 28 (1988) 193-199
10. Wakatsuki M., and Tomita E. A fast algorithm for checking the inclusion for very simple deterministic pushdown automata. IEICE transactions on information and systems E 76-D 10 (1993) 1224-1233
11. Wakatsuki M., Teraguchi K., and Tomita E. Polynomial time identification of strict deterministic restricted one-counter automata in some class from positive data. In: Paliouras G., and Sakakibara Y. (Eds). ICGI. Lecture Notes in Computer Science vol. 3264 (2004), Springer 260-272
12. Wiehagen R., and Zeugmann T. Ignoring data may be the only way to learn efficiently. Journal of Experimental and Theoretical Computer Science 6 (1994) 131-144
13. Yokomori T. On polynomial-time learnability in the limit of strictly deterministic automata. Machine Learning 19 (1995) 153-179
14. Yokomori T. Polynomial-time identification of very simple grammars from positive data. Theoretical Computer Science 298 (2003) 179-206
15. Yokomori T. Erratum to "Polynomial-time identification of very simple grammars from positive data" [Theoret. Comput. Sci. 298 (2003) 179-206]. Theoretical Computer Science 377 1-3 (2007) 282-283
16. Yoshinaka R. Polynomial-time identification of an extension of very simple grammars from positive data. In: Sakakibara Y., Kobayashi S., Sato K., Nishino T., and Tomita E. (Eds). ICGI. Lecture Notes in Computer Science vol. 4201 (2006), Springer 45-58
17. Yoshinaka R. Learning efficiency of very simple grammars from positive data. Proceedings of the 17th International Conference on Algorithmic Learning Theory. LNCS (LNAI) vol. 4754 (2007) 227-241
18. Zeugmann T., and Lange S. A guided tour across the boundaries of learning recursive languages. Algorithmic Learning for Knowledge-Based Systems. Lecture Notes in Artificial Intelligence vol. 961 (1995), Springer 190-258