Advertisement

Learning from a Smarter Teacher

  • Leonor Becerra-Bonache
  • Adrian Horia Dediu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5788)

Abstract

We present a new learning algorithm introducing a helpful teacher who models the learners’ knowledge. Our algorithm, called learning from extensions (LEX), learns finite-state transducers using only one type of query called extension query. Our query was inspired by equivalence queries and counterexamples, but we show in this article that it is possible to learn efficiently finite state transducers using only extension queries (it is known that only with membership queries or only with equivalence queries is not possible). The teacher answers an extension query by connecting the new information asked by the learner with the information that the learner already knows. We prove that our new algorithm LEX discovers a target finite-state transducer in polynomial time. We also discuss briefly several complexity aspects and we give an example.

Keywords

Mathematical Linguistics Automaton Learn Input Alphabet Membership Query Deterministic Finite Automaton 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Gold, E.M.: Language identification in the limit. Information and Control 10(5), 447–474 (1967)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Angluin, D.: Learning regular sets from queries and counterexamples. Information and Computation 75(2), 87–106 (1987)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Valiant, L.G.: A theory of the learnable. Communications of the ACM 27(11), 1134–1142 (1984)CrossRefzbMATHGoogle Scholar
  4. 4.
    Rivest, R.L., Schapire, R.E.: Inference of finite automata using homing sequences. In: STOC 1989: Proceedings of the twenty-first annual ACM symposium on Theory of computing, pp. 411–420. ACM Press, New York (1989)CrossRefGoogle Scholar
  5. 5.
    Balcazar, J.L., Diaz, J., Gavalda, R.: Algorithms for learning finite automata from queries: A unified view. In: Advances in Algorithms, Languages, and Complexity, pp. 53–72 (1997)Google Scholar
  6. 6.
    Angluin, D.: Negative results for equivalence queries. Machine Learning 5(2), 121–150 (1990)Google Scholar
  7. 7.
    Becerra-Bonache, L., Dediu, A.H., Tîrnăucă, C.: Learning DFA from correction and equivalence queries. In: Sakakibara, Y., Kobayashi, S., Sato, K., Nishino, T., Tomita, E. (eds.) ICGI 2006. LNCS (LNAI), vol. 4201, pp. 281–292. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  8. 8.
    Hopcroft, J.E., Ullman, J.D.: Introduction to Automata Theory, Languages, and Computation. Addison-Wesley, Massachusetts (1979)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Leonor Becerra-Bonache
    • 1
    • 2
  • Adrian Horia Dediu
    • 1
    • 3
  1. 1.Research Group on Mathematical LinguisticsRovira i Virgili UniversityTarragonaSpain
  2. 2.Department of Computer ScienceYale UniversityNew HavenUSA
  3. 3.“Politehnica” University of BucharestBucharestRomania

Personalised recommendations