From learning in the limit to stochastic finite learning

Theoretical Computer Science

Volumn 364, Issue 1, 2006, Pages 77-97

  Zeugmann, Thomas ^a  

^a HOKKAIDO UNIVERSITY   (Japan)

Author keywords

Average case analysis; Inductive learning; Iterative learning; Learning conjunctive concepts; Learning in the limit; Pattern languages; Stochastic finite learning

Indexed keywords

AVERAGE-CASE ANALYSIS; INDUCTIVE LEARNING; ITERATIVE LEARNING; LEARNING CONJUNCTIVE CONCEPTS; LEARNING IN THE LIMIT; PATTERN LANGUAGES; STOCHASTIC FINITE LEARNING;

COMPUTATIONAL GRAMMARS; LEARNING SYSTEMS; LINGUISTICS; QUERY LANGUAGES; STOCHASTIC MODELS; SURVEYS;

EDUCATION;

EID: 43049141572     PISSN: 03043975     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.tcs.2006.07.042     Document Type: Article

References (53)

1. Angluin D. Finding patterns common to a set of strings. J. Comput. Syst. Sci. 21 1 (1980) 46-62
2. Angluin D. Inductive inference of formal languages from positive data. Inform. and Control 45 2 (1980) 117-135
3. Angluin D., and Smith C.H. Inductive inference: theory and methods. Comput. Surveys 15 3 (1983) 237-269
4. Angluin D., and Smith C.H. Formal inductive inference. In: Shapiro St.C. (Ed). Encyclopedia of Artificial Intelligence Vol. 1 (1987), Wiley-Interscience Publication, New York 409-418
5. Arikawa S., Shinohara T., and Yamamoto A. Learning elementary formal systems. Theoret. Comput. Sci. 95 1 (1992) 97-113
6. Blum L., and Blum M. Toward a mathematical theory of inductive inference. Inform. and Control 28 2 (1975) 125-155
7. Blumer A., Ehrenfeucht A., Haussler D., and Warmuth M. Learnability and the Vapnik-Chervonenkis dimension. J. ACM 36 (1989) 929-965
8. Bratko I., and Muggleton S. Applications of inductive logic programming. Commun. ACM 38 11 (1995) 65-70
9. Case J., Jain S., Lange S., and Zeugmann T. Incremental concept learning for bounded data mining. Inform. and Comput. 152 1 (1999) 74-110
10. Daley R.P., and Smith C.H. On the complexity of inductive inference. Inform. and Control 69 1-3 (1986) 12-40
11. Erlebach T., Rossmanith P., Stadtherr H., Steger A., and Zeugmann T. Learning one-variable pattern languages very efficiently on average, in parallel, and by asking queries. Theoret. Comput. Sci. 261 1/2 (2001) 119-156
12. Gold E.M. Language identification in the limit. Inform. and Control 10 5 (1967) 447-474
13. Goldman S.A., Kearns M.J., and Schapire R.E. Exact identification of circuits using fixed points of amplification functions. SIAM J. Comput. 22 4 (1993) 705-726
14. Haussler D. Bias, version spaces and Valiant's learning framework. Proc. Fourth Internat. Workshop on Machine Learning (1987), Morgan Kaufmann, San Mateo, CA 324-336
15. Haussler D., Kearns M., Littlestone N., and Warmuth M.K. Equivalence of models for polynomial learnability. Inform. and Comput. 95 2 (1991) 129-161
16. Jain S., Osherson D., Royer J.S., and Sharma A. Systems That Learn: An Introduction to Learning Theory (1999), MIT Press, Boston, MA
17. T. Jiang, A. Salomaa, K. Salomaa, S. Yu, Inclusion is undecidable for pattern languages, in: A. Lingas, R. Karlsson, S. Carlsson (Eds.), Automata, Languages and Programming, 20th Internat. Colloquium, ICALP 93, Lund, Sweden, July 5-9, 1993, Proc., Lecture Notes in Computer Science, Vol. 700, Springer, Berlin, 1993, pp. 301-312.
18. Kearns M., and Pitt L. A polynomial-time algorithm for learning k-variable pattern languages from examples. In: Rivest R., Haussler D., and Warmuth M.K. (Eds). Proc. Second Annu. Workshop on Computational Learning Theory. Santa Cruz, California (1989), Morgan Kaufmann, San Mateo, CA 57-71
19. Lange S., and Wiehagen R. Polynomial-time inference of arbitrary pattern languages. New Generation Comput. 8 4 (1991) 361-370
20. S. Lange, T. Zeugmann, Language learning in dependence on the space of hypotheses, in: L. Pitt (Ed.), Proc. Sixth Annu. ACM Conf. on Computational Learning Theory, July 26th-28th, 1993, Santa Cruz, California, ACM Press, New York, 1993, pp. 127-136.
21. Lange S., and Zeugmann T. Set-driven and rearrangement-independent learning of recursive languages. Math. Systems Theory 29 6 (1996) 599-634
22. Lange S., and Zeugmann T. Incremental learning from positive data. J. Comput. System Sci. 53 1 (1996) 88-103
23. Lavrač N., and Džeroski S. Inductive Logic Programming: Techniques and Applications (1994), Ellis Horwood, Chichester, UK
24. Mitchell T. Machine Learning (1997), McGraw-Hill, New York
25. A. Mitchell, A. Sharma, T. Scheffer, F. Stephan, The VC-dimension of subclasses of pattern languages, in: O. Watanabe, T. Yokomori (Eds.), Algorithmic Learning Theory, 10th Internat. Conf., ALT '99, Tokyo, Japan, December 1999, Proc., Lecture Notes in Artificial Intelligence, Vol. 1720, Springer, Berlin, 1999, pp. 93-105.
26. Miyano S., Shinohara A., and Shinohara T. Polynomial-time learning of elementary formal systems. New Generation Comput. 18 3 (2000) 217-242
27. S. Muggleton, Bayesian inductive logic programming, in: M. Warmuth (Ed.), Proc. Seventh Annu. ACM Conf. on Computational Learning Theory, July 12th-15th, 1994, New Brunswick, New Jersey, ACM Press, New York, 1994, pp. 3-11.
28. Muggleton S., and De Raedt L. Inductive logic programming: theory and methods. J. Logic Programming 19/20 (1994) 669-679
29. R.P. Nix, Editing by Examples, Yale University, Department of Computer Science, Technical Report 280, 1983.
30. Osherson D.N., Stob M., and Weinstein S. Systems that Learn, An Introduction to Learning Theory for Cognitive and Computer Scientists (1986), MIT Press, Cambridge, MA
31. A. Salomaa, Patterns (The Formal Language Theory Column), EATCS Bull. 54 (1994) 46-62.
32. L. Pitt, Inductive inference, DFAs and computational complexity, in: K.P. Jantke (Ed.), Analogical and Inductive Inference, Internat. Workshop AII '89, Reinhardsbrunn Castle, GDR, October 1989, Proc., Lecture Notes in Artificial Intelligence, Vol. 397, Springer, Berlin, 1989, pp. 18-44.
33. R. Reischuk, T. Zeugmann, Learning one-variable pattern languages in linear average time, in: Proc. 11th Annu. Conf. on Computational Learning Theory, July 24th-26th, 1998, Madison, Wisconsin, ACM Press, New York, 1998, pp. 198-208.
34. R. Reischuk, T. Zeugmann, A complete and tight average-case analysis of learning monomials, in: C. Meinel, S. Tison (Eds.), STACS 99, 16th Annu. Symp. on Theoretical Aspects of Computer Science, Trier, Germany, March 1999, Proc., Lecture Notes in Computer Science, Vol. 1563, Springer, Berlin, 1999, pp. 414-423.
35. Reischuk R., and Zeugmann T. An average-case optimal one-variable pattern language learner. J. Comput. System Sci. 60 2 (2000) 302-335
36. A. Salomaa, Return to patterns (The Formal Language Theory Column), EATCS Bull. 55 (1994) 144-157.
37. Rogers Jr. H. Theory of Recursive Functions and Effective Computability (1967), McGraw-Hill, New York
38. Rossmanith P., and Zeugmann T. Stochastic finite learning of the pattern languages. Mach. Learn. 44 1-2 (2001) 67-91
39. Schapire R.E. Pattern languages are not learnable. In: Fulk M.A., and Case J. (Eds). Proc. Third Annu. Workshop on Computational Learning Theory (1990), Morgan Kaufmann, San Mateo, CA 122-129
40. Shimozono S., Shinohara A., Shinohara T., Miyano S., Kuhara S., and Arikawa S. Knowledge acquisition from amino acid sequences by machine learning system BONSAI. Trans. Inform. Process. Soc. Japan 35 (1994) 2009-2018
41. Shinohara T. Inferring unions of two pattern languages. Bull. Inform. Cybernet. 20 (1983) 83-88
42. Shinohara T. Inductive inference of monotonic formal systems from positive data. New Generation Comput. 8 4 (1991) 371-384
43. T. Shinohara, S. Arikawa, Pattern inference, in: K.P. Jantke, S. Lange (Eds.), Algorithmic Learning for Knowledge-Based Systems, Lecture Notes in Artificial Intelligence, Vol. 961, Springer, Berlin, 1995, pp. 259-291.
44. T. Shinohara, H. Arimura, Inductive inference of unbounded unions of pattern languages from positive data, in: S. Arikawa, A.K. Sharma (Eds.), Algorithmic Learning Theory, Seventh Internat. Workshop, ALT '96, Sydney, Australia, October 1996, Proc., Lecture Notes in Artificial Intelligence, Vol. 1160, Springer, Berlin, 1996, pp. 256-271.
45. R. Smullyan, Theory of formal systems, Annals of Mathematical Studies, Vol. 47, Princeton, NJ, 1961.
46. Valiant L.G. A theory of the learnable. Commun. ACM 27 11 (1984) 1134-1142
47. Wiehagen R. Limes-Erkennung rekursiver Funktionen durch spezielle Strategien. J. Inform. Process. Cybernet. (EIK) 12 1/2 (1976) 93-99
48. Wiehagen R., and Zeugmann T. Ignoring data may be the only way to learn efficiently. J. Experim. Theoret. Artificial Intelligence 6 1 (1994) 131-144
49. R. Wiehagen, T. Zeugmann, Learning and consistency, in: K.P. Jantke, S. Lange (Eds.), Algorithmic Learning for Knowledge-Based Systems, Lecture Notes in Artificial Intelligence, Vol. 961, Springer, Berlin, 1995, pp. 1-24.
50. K. Wright, Identification of unions of languages drawn from an identifiable class, in: R. Rivest, D. Haussler, M.K. Warmuth (Eds.), Proc. Second Annu. Workshop on Computational Learning Theory, Santa Cruz, California, Morgan Kaufmann, San Mateo, CA, 1989, pp. 328-333.
51. Zeugmann T. Lange and Wiehagen's pattern language learning algorithm: an average-case analysis with respect to its total learning time. Annals of Math. Artificial Intelligence 23 1-2 (1998) 117-145
52. T. Zeugmann, S. Lange, A guided tour across the boundaries of learning recursive languages, in: K.P. Jantke, S. Lange (Eds.), Algorithmic Learning for Knowledge-Based Systems, Lecture Notes in Artificial Intelligence, Vol. 961, Springer, Berlin, 1995, pp. 190-258.
53. Zeugmann T., Lange S., and Kapur S. Characterizations of monotonic and dual monotonic language learning. Inform. and Comput. 120 2 (1995) 155-173