Learning a subclass of regular patterns in polynomial time

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

Volumn 2842, Issue , 2003, Pages 234-246

  Case, John ^a   Jain, Sanjay ^b   Reischuk, Rüdiger ^c   Stephan, Frank ^d   Zeugmann, Thomas ^c  

^a University of Delaware   (United States)

^b NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^c UNIVERSITY OF LÜBECK   (Germany)

^d UNIVERSITY OF HEIDELBERG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

POLYNOMIAL APPROXIMATION; PROBABILITY DISTRIBUTIONS; ALGORITHMS; POLYNOMIALS; PROBABILISTIC LOGICS; PROBABILITY;

HIGH PROBABILITY; PLAUSIBLE ASSUMPTIONS; POLYNOMIAL-TIME; REGULAR PATTERNS; FIXED LENGTH VARIABLES; POLYNOMIAL TIME; REGULAR PATTERN LANGUAGES;

C (PROGRAMMING LANGUAGE); LEARNING SYSTEMS;

EID: 7444221857     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-39624-6_19     Document Type: Conference Paper

References (33)

1. D. Angluin. Finding patterns common to a set of strings. Journal of Computer and System Sciences, 21:46-62, 1980.
2. S. Arikawa, T. Shinohara, and A. Yamamoto. Learning elementary formal systems. Theoretical Computer Science, 95:97-113, 1992.
3. T. Shinohara and H. Arimura. Inductive inference of unbounded unions of pattern languages from positive data. Theoretical Computer Science, 241:191-209, 2000.
4. I. Bratko and S. Muggleton. Applications of inductive logic programming. Communications of the ACM, 1995.
5. A. Brāzma, E. Ukkonen, and J. Vilo. Discovering unbounded unions of regular pattern languages from positive examples. In Proceedings of the 7th International Symposium on Algorithms and Computation (ISAAC’96), volume 1178 of Lecture Notes in Computer Science, pages 95-104, Springer, 1996.
6. J. Case, S. Jain, S. Kaufmann, A. Sharma, and F. Stephan. Predictive learning models for concept drift. Theoretical Computer Science, 268:323-349, 2001. Special Issue for ALT’98.
7. J. Case, S. Jain, S. Lange, and T. Zeugmann. Incremental concept learning for bounded data mining. Information and Computation, 152(1):74-110, 1999.
8. T. Erlebach, P. Rossmanith, H. Stadtherr, A. Steger, and T. Zeugmann. Learning one-variable pattern languages very efficiently on average, in parallel, and by asking queries. Theoretical Computer Science, 261(1):119-156, 2001.
9. E. M. Gold. Language identification in the limit. Information & Control, 10:447-474, 1967.
10. M. Kearns and L. Pitt. A polynomial-time algorithm for learning k-variable pattern languages from examples. In R. Rivest, D. Haussler and M. K. Warmuth (Eds.), Proceedings of the Second Annual ACM Workshop on Computational Learning Theory, pages 57-71, Morgan Kaufmann Publishers Inc., 1989.
11. P. Kilpeläinen, H. Mannila, and E. Ukkonen. MDL learning of unions of simple pattern languages from positive examples. In Paul Vitányi, editor, Second European Conference on Computational Learning Theory, volume 904 of Lecture Notes in Artificial Intelligence, pages 252–260. Springer, 1995.
12. S. Lange and R. Wiehagen. Polynomial time inference of arbitrary pattern languages. New Generation Computing, 8:361-370, 1991.
13. N. Lavrač and S. Džeroski. Inductive Logic Programming: Techniques and Applications. Ellis Horwood, 1994.
14. S. Matsumoto and A. Shinohara. Learnability of subsequence languages. Information Modeling and Knowledge Bases VIII, pages 335-344, IOS Press, 1997.
15. T. Mitchell. Machine Learning. McGraw Hill, 1997.
16. S. Miyano, A. Shinohara and T. Shinohara. Polynomial-time learning of elementary formal systems. New Generation Computing, 18:217-242, 2000.
17. S. Muggleton and L. De Raedt. Inductive logic programming: Theory and methods. Journal of Logic Programming, 19/20:669-679, 1994.
18. R. Nix. Editing by examples. Technical Report 280, Department of Computer Science, Yale University, New Haven, CT, USA, 1983.
19. D. Reidenbach. A Negative Result on Inductive Inference of Extended Pattern Languages. In N. Cesa-Bianchi and M. Numao, editors, Algorithmic Learning Theory, 13th International Conference, ALT 2002, Lübeck, Germany, November 2002, Proceedings, pages 308–320. Springer, 2002.
20. R. Reischuk and T. Zeugmann. Learning one-variable pattern languages in linear average time. In Proceedings of the Eleventh Annual Conference on Computational Learning Theory, pages 198–208. ACM Press, 1998.
21. P. Rossmanith and T. Zeugmann. Stochastic Finite Learning of the Pattern Languages. Machine Learning 44(1/2):67-91, 2001. Special Issue on Automata Induction, Grammar Inference, and Language Acquisition
22. A. Salomaa. Patterns (The Formal Language Theory Column). EATCS Bulletin, 54:46-62, 1994.
23. A. Salomaa. Return to patterns (The Formal Language Theory Column). EATCS Bulletin, 55:144-157, 1994.
24. R. Schapire, Pattern languages are not learnable. In M. A. Fulk and J. Case, editors, Proceedings, 3rd Annual ACM Workshop on Computational Learning Theory, pages 122-129, Morgan Kaufmann Publishers, Inc., 1990.
25. S. Shimozono, A. Shinohara, T. Shinohara, S. Miyano, S. Kuhara, and S. Arikawa. Knowledge acquisition from amino acid sequences by machine learning system BONSAI. Trans. Information Processing Society of Japan, 35:2009-2018, 1994.
26. T. Shinohara. Polynomial time inference of extended regular pattern languages. In RIMS Symposia on Software Science and Engineering, Kyoto, Japan, volume 147 of Lecture Notes in Computer Science, pages 115–127. Springer-Verlag, 1982.
27. T. Shinohara. Inferring unions of two pattern languages. Bulletin of Informatics and Cybernetics, 20:83–88., 1983.
28. T. Shinohara and S. Arikawa. Learning data entry systems: An application of inductive inference of pattern languages. Research Report 102, Research Institute of Fundamental Information Science, Kyushu University, 1983.
29. T. Shinohara and S. Arikawa. Pattern inference. In Klaus P. Jantke and Steffen Lange, editors, Algorithmic Learning for Knowledge-Based Systems, volume 961 of Lecture Notes in Artificial Intelligence, pages 259–291. Springer, 1995.
30. R. Smullyan. Theory of Formal Systems, Annals of Mathematical Studies, No. 47. Princeton, NJ, 1961.
31. L. G. Valiant. A theory of the learnable. Communications of the ACM 27:1134-1142, 1984.
32. 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, pages 328–333. Morgan Kaufmann Publishers, Inc., 1989.
33. T. Zeugmann. Lange and Wiehagen’s pattern language learning algorithm: An average-case analysis with respect to its total learning time. Annals of Mathematics and Artificial Intelligence, 23(1–2):117-145, 1998.