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Generalizing over Several Learning Settings

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Grammatical Inference: Theoretical Results and Applications (ICGI 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6339))

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Abstract

We recapitulate inference from membership and equivalence queries, positive and negative samples. Regular languages cannot be learned from one of those information sources only [1,2,3]. Combinations of two sources allowing regular (polynomial) inference are MQs and EQs [4], MQs and positive data [5,6], positive and negative data [7,8]. We sketch a meta-algorithm fully presented in [9] that generalizes over as many combinations of those sources as possible. This includes a survey of pairings for which there are no well-studied algorithms.

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References

  1. Gold, E.: Language identification in the limit. Inf. & Contr. 10(5), 447–474 (1967)

    Article  MATH  Google Scholar 

  2. Angluin, D.: Queries and concept learning. Mach. L. 2, 319–342 (1988)

    Google Scholar 

  3. Angluin, D.: Negative results for equivalence queries. Mach. L. 5, 121–150 (1990)

    Google Scholar 

  4. Angluin, D.: Learning regular sets from queries and counterexamples. Information and Computation 75(2), 87–106 (1987)

    Article  MATH  MathSciNet  Google Scholar 

  5. Angluin, D.: A note on the number of queries needed to identify regular languages. Inf. & Contr. 51, 76–87 (1981)

    Article  MATH  MathSciNet  Google Scholar 

  6. Besombes, J., Marion, J.Y.: Learning tree languages from positive examples and membership queries. In: Gavaldá, R., Jantke, K.P., Takimoto, E. (eds.) ALT 2003. LNCS (LNAI), vol. 2842, pp. 440–453. Springer, Heidelberg (2003)

    Google Scholar 

  7. Oncina, J., Garcia, P.: Identifying regular languages in polynomial time. Machine Perception and Artificial Intelligence, vol. 5, pp. 99–108. World Scientific, Singapore (2002)

    Google Scholar 

  8. de la Higuera, C.: Grammatical Inference: Learning Automata and Grammars. Cambridge University Press, Cambridge (2010)

    MATH  Google Scholar 

  9. Kasprzik, A.: Generalizing over several learning settings. Technical report, University of Trier (2009)

    Google Scholar 

  10. Drewes, F., Högberg, J.: Learning a regular tree language from a teacher. In: Ésik, Z., Fülöp, Z. (eds.) DLT 2003. LNCS, vol. 2710, pp. 279–291. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  11. Oncina, J., Garcia, P.: Inference of recognizable tree sets. Technical report, DSIC II/47/93, Universidad de Valencia (1993)

    Google Scholar 

  12. Kasprzik, A.: A learning algorithm for multi-dimensional trees, or: Learning beyond context-freeness. In: Clark, A., Coste, F., Miclet, L. (eds.) ICGI 2008. LNCS (LNAI), vol. 5278, pp. 111–124. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  13. Tîrnăucă, C.: A note on the relationship between different types of correction queries. In: Clark, A., Coste, F., Miclet, L. (eds.) ICGI 2008. LNCS (LNAI), vol. 5278, pp. 213–223. Springer, Heidelberg (2008)

    Google Scholar 

  14. Pitt, L.: Inductive inference, DFAs, and computational complexity. In: Jantke, K.P. (ed.) AII 1989. LNCS, vol. 397. Springer, Heidelberg (1989)

    Google Scholar 

  15. Fernau, H.: Identification of function distinguishable languages. Theoretical Computer Science 290(3), 1679–1711 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  16. Fernau, H.: Even linear simple matrix languages: Formal language properties and grammatical inference. Theoretical Computer Science 289(1), 425–456 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  17. Berman, P., Roos, R.: Learning one-counter languages in polynomial time. In: SFCS, pp. 61–67 (1987)

    Google Scholar 

  18. Yoshinaka, R.: Learning mildly context-sensitive languages with multidimensional substitutability from positive data. In: Gavaldà, R., Lugosi, G., Zeugmann, T., Zilles, S. (eds.) ALT 2009. LNCS, vol. 5809, pp. 278–292. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  19. Clark, A.: Three learnable models for the description of language. In: Dediu, A.-H., Fernau, H., Martín-Vide, C. (eds.) LATA 2010. LNCS, vol. 6031, pp. 16–31. Springer, Heidelberg (2010)

    Google Scholar 

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Authors and Affiliations

  1. University of Trier,  

    Anna Kasprzik

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  1. Anna Kasprzik
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  1. Departamento de Sistemas Informáticos y Computatión, Universidad Politécnica de Valencia, Camino de Vera s/n, 46022, Valencia, Spain

    José M. Sempere  & Pedro García  & 

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Kasprzik, A. (2010). Generalizing over Several Learning Settings. In: Sempere, J.M., García, P. (eds) Grammatical Inference: Theoretical Results and Applications. ICGI 2010. Lecture Notes in Computer Science(), vol 6339. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15488-1_28

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  • DOI: https://doi.org/10.1007/978-3-642-15488-1_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15487-4

  • Online ISBN: 978-3-642-15488-1

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