Inductive Inference and Language Learning

  • Thomas Zeugmann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3959)

Abstract

The present paper is a short reflection concerning the role which inductive inference played and can play in language learning. We shortly recall some major insights obtained and outline some new directions based on own work and results recently presented in the literature.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Adriaans, P.W., Trautwein, M., Vervoort, M.: Towards high speed grammar induction on large text corpora. In: Jeffery, K., Hlaváč, V., Wiedermann, J. (eds.) SOFSEM 2000. LNCS, vol. 1963, pp. 173–186. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  2. 2.
    Angluin, D.: Finding patterns common to a set of strings. J. of Comput. Syst. Sci. 21(1), 46–62 (1980)MATHCrossRefMathSciNetGoogle Scholar
  3. 3.
    Angluin, D.: Inductive inference of formal languages from positive data. Inform. Control 45(2), 117–135 (1980)MATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Clark, A., Eyraud, R.: Identification in the limit of substitutable contextfree languages. In: Jain, S., Simon, H.U., Tomita, E. (eds.) ALT 2005. LNCS (LNAI), vol. 3734, pp. 283–296. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  5. 5.
    de la Higuera, C.: Characteristic sets for polynomial grammatical inference. Machine Learning 27, 125–138 (1997)MATHCrossRefGoogle Scholar
  6. 6.
    Freivalds, R., Kinber, E.B., Wiehagen, R.: On the power of inductive inference from good examples. Theoret. Comput. Sci. 110(1), 131–144 (1993)MATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Gold, E.M.: Language identification in the limit. Inform. Control 10(5), 447–474 (1967)MATHCrossRefGoogle Scholar
  8. 8.
    Rogers, J.H.: Theory of Recursive Functions and Effective Computability. McGraw-Hill, New York (1967)MATHGoogle Scholar
  9. 9.
    Hopcroft, J., Ullman, J.: Formal Languages and their Relation to Automata. Addison-Wesley, Reading (1969)MATHGoogle Scholar
  10. 10.
    Jain, S., Osherson, D., Royer, J.S., Sharma, A.: Systems that Learn: An Introduction to Learning Theory, 2nd edn. MIT Press, Cambridge (1999)Google Scholar
  11. 11.
    Joachims, T.: Learning to Classify Text using Support Vector Machines: Methods, Theory, and Algorithms. Kluwer Academic Publishers, Dordrecht (2002)Google Scholar
  12. 12.
    Lange, S., Nessel, J., Wiehagen, R.: Learning recursive languages from good examples. Annals of Mathematics and Artificial Intelligence 23(1/2), 27–52 (1998)MATHCrossRefMathSciNetGoogle Scholar
  13. 13.
    Lange, S., Wiehagen, R.: Polynomial-time inference of arbitrary pattern languages. New Generation Computing 8(4), 361–370 (1991)MATHCrossRefGoogle Scholar
  14. 14.
    Osherson, D.N., Stob, M., Weinstein, S.: Systems that Learn: An Introduction to Learning Theory for Cognitive and Computer Scientists. MIT Press, Cambridge (1986)Google Scholar
  15. 15.
    Reischuk, R., Zeugmann, T.: An average-case optimal one-variable pattern language learner. J. Comput. Syst. Sci. 60(2), 302–335 (2000)MATHCrossRefMathSciNetGoogle Scholar
  16. 16.
    Rossmanith, P., Zeugmann, T.: Stochastic finite learning of the pattern languages. Machine Learning 44(1/2), 67–91 (2001)MATHCrossRefGoogle Scholar
  17. 17.
    Shinohara, T.: Rich classes inferable from positive data: Length-bounded elementary formal systems. Inform. Comput. 108(2), 175–186 (1994)MATHCrossRefMathSciNetGoogle Scholar
  18. 18.
    Shinohara, T., Arikawa, S.: Pattern inference. In: Algorithmic Learning for Knowledge-Based Systems. LNCS (LNAI), vol. 961, pp. 259–291. Springer, Heidelberg (1995)Google Scholar
  19. 19.
    Wiehagen, R., Zeugmann, T.: Learning and consistency. In: Lange, S., Jantke, K.P. (eds.) GOSLER 1994. LNCS (LNAI), vol. 961, pp. 1–24. Springer, Heidelberg (1995)Google Scholar
  20. 20.
    Yokomori, T.: Polynomial-time identification of very simple grammars from positive data. Theoret. Comput. Sci. 298(1), 179–206 (2003)MATHCrossRefMathSciNetGoogle Scholar
  21. 21.
    Zeugmann, T.: 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, 117–145 (1998)MATHCrossRefMathSciNetGoogle Scholar
  22. 22.
    Zeugmann, T., Lange, S.: A guided tour across the boundaries of learning recursive languages. In: Algorithmic Learning for Knowledge-Based Systems. LNCS (LNAI), vol. 961, pp. 190–258. Springer, Heidelberg (1995)Google Scholar
  23. 23.
    Zeugmann, T., Lange, S., Kapur, S.: Characterizations of monotonic and dual monotonic language learning. Inform. Comput. 120(2), 155–173 (1995)MATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Thomas Zeugmann
    • 1
  1. 1.Division of Computer ScienceHokkaido UniversitySapporoJapan

Personalised recommendations