Ordinal mind change complexity of language identification

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

Volumn 1208, Issue , 1997, Pages 301-315

  Ambainis, Andris ^a   Jain, Sanjay ^b   Sharma, Arun ^c  

^a UNIVERSITY OF LATVIA   (Latvia)

^b NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^c UNIVERSITY OF NEW SOUTH WALES   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

LOGIC PROGRAMMING;

ELEMENTARY FORMAL SYSTEMS; FINITE ELASTICITY; FINITE THICKNESS; INDEXED FAMILIES; LANGUAGE IDENTIFICATION; LEARNING MACHINES; MIND CHANGE COMPLEXITY; PATTERN LANGUAGES;

MACHINE LEARNING;

EID: 84949211630     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-62685-9_25     Document Type: Conference Paper

References (20)

1. +92] S. Arikawa, S. Miyano, A. Shinohara, T. Shinohara, and A. Yamamoto. Algorithmic learning theory with elementary formal systems. IEICE Trans. Inf. and Syst., E75-D No. 4:405-414, 1992.
2. [Ang80] D. Angluin. Finding patterns common to a set of strings. Journal of Computer and System Sciences, 21:46-62, 1980.
3. [CJS95] J. Case, S. Jain, and M. Suraj. Not-so-nearly-minimal-size program inference. In Klaus P. Jantke and Steffen Lange, editors, Algorithmic Learning for Knowledge-Based Systems, volume 961 of Lecture Notes in Artificial Intelligence, pages 77-96. Springer-Verlag, 1995.
4. [FS93] R. Freivalds and C. Smith. On the role of procrastination in machine learning. Information and Computation, pages 237-271, 1993.
5. [Gol67] E. M. Gold. Language identification in the limit. Information and Control, 10:447-474, 1967.
6. [Jan91] K. P. Jantke. Monotonic and non-monotonic inductive inference. New Generation Computing, 8:349-360, 1991.
7. [JS94] S. Jain and A. Sharma. On the intrinsic complexity of language identification. In Proceedings of the Seventh Annual Conference on Computational Learning Theory, New Brunswick, New Jersey, pages 278-286. ACM-Press, July 1994.
8. [JS96] S. Jain and A. Sharma. Elementary formal systems, intrinsic complexity, and procrastination. In Proceedings of the Ninth Annual Conference on Computational Learning Theory, pages 181-192. ACM-Press, June 1996.
9. [Kle38] S. C. Kleene. Notations for ordinal numbers. Journal of Symbolic Logic, 3:150155, 1938.
10. [LZ93] S. Lange and T. Zeugmann. Monotonic versus non-monotonic language learning. In Proceedings of the Second International Workshop on Nonmonotonic and Inductive Logic, pages 254-269. Springer-Verlag, 1993. Lecture Notes in Artificial Intelligence 659.
11. [MSW91] T. Motoki, T. Shinohara, and K. Wright. The correct definition of finite elasticity: Corrigendum to identification of unions. In L. Valiant and M. Warmuth, editors, Proceedings of the Fourth Annual Workshop on Computational Learning Theory, Santa Cruz, California, page 375. Morgan Kaufman, 1991.
12. [Muk94] Y. Mukouchi. Inductive inference of an approximate concept from positive data. In S. Arikawa and K. P. Jantke, editors, Algorithmic Learning Theory, 4th International Workshop on Analogical and Inductive Inference, AII'94 and 5th International Workshop on Algorithm Learning Theory, ALT'94, Lecture Notes in Artificial Intelligence, 872, pages 484-499. Springer-Verlag, 1994.
13. [OSW86] D. Osherson, M. Stob, and S. Weinstein. Systems that Learn, An Introduction to Learning Theory for Cognitive and Computer Scientists. MIT Press, Cambridge, Mass., 1986.
14. [Rog67] H. Rogers. Theory of Recursive Functions and Effective Computability. McGraw-Hill, New York, 1967. Reprinted, MIT Press 1987.
15. [Sac90] G. E. Sacks. Higher Recursion Theory. Springer-Verlag, 1990.
16. [Shi86] T. Shinohara. Studies on Inductive Inference from Positive Data. PhD thesis, Kyushu University, Kyushu, Japan, 1986.
17. [Shi94] T. Shinohara. Rich classes inferable from positive data: Length-bounded elementary formal systems. Information and Computation, 108:175-186, 1994.
18. [SM94] M. Sato and T. Moriyama. Inductive inference of length bounded EFS's from positive data. Technical Report DMSIS-RR-94-2, Department of Mathematical Sciences and Information Sciences, University of Osaka Prefecture, Japan, 1994.
19. [Wri89] K. Wright. Identification of unions of languages drawn from an identifiable class. In R. Rivest, D. Haussler, and M. K. Warmuth, editors, Proceedings of the Second Annual Workshop on Computational Learning Theory, Santa Cruz, California, pages 328-333. Morgan Kaufmann Publishers, Inc., 1989.
20. [ZL95] T. Zeugmann and S. Lange. A guided tour across the boundaries of learning recursive languages. In K.P. Jantke and S. Lange, editors, Algorithmic Learning for Knowledge-Based Systems, pages 190-258. Lecture Notes in Artificial Intelligence No. 961, Springer-Verlag, 1995.