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Learning Locally Testable Even Linear Languages from Positive Data

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Grammatical Inference: Algorithms and Applications (ICGI 2002)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2484))

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Abstract

Learning from positive data is a center goal in grammatical inference. Some language classes have been characterized in order to allow its learning from text. There are two different approaches to this topic: (i) reducing the new classes to well known ones, and (ii) designing new learning algorithms for the new classes. In this work we will use reduction techniques to define new classes of even linear languages which can be inferred from positive data only. We will center our attention to inferable classes based on local testability features. So, the learning processes for such classes of even linear languages can be performed by using algorithms for locally testable regular languages.

Work supported by the Spanish CICYT under contract TIC2000-1153.

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References

  1. V. Amar and G. Putzolu. On a Family of Linear Grammars. Information and Control 7, pp 283–291. 1964.

    Article  MATH  MathSciNet  Google Scholar 

  2. D. Angluin. Inductive Inference of Formal Languages from Positive Data. Information and Control 45, pp 117–135. 1980.

    Article  MATH  MathSciNet  Google Scholar 

  3. D. Angluin. Inference of Reversible Languages. Journal of the Association for Computing Machinery. Vol 29 No 3, pp 741–765. July 1982.

    Google Scholar 

  4. D. Angluin and C. Smith. Inductive Inference: Theory and Methods. Computing Surveys, vol. 15. No. 3, pp 237–269. 1983.

    Article  MathSciNet  Google Scholar 

  5. J.D. Emerald, K.G. Subramanian and D.G. Thomas. Learning code regular and code linear languages. Proceedings of the Third International Colloquium on Grammatical Inference ICGI-96. (L. Miclet, C. de la Higuera, eds). LNAI Vol. 1147, pp 211–221. Springer. 1996.

    Google Scholar 

  6. J.D. Emerald, K.G. Subramanian and D.G. Thomas. Learning a subclass of context-free Languages. Proceedings of the 4th International Colloquium ICGI-98. (V. Honavar, G. Slutzki, eds). LNAI Vol. 1433, pp 223–243. 1998.

    Google Scholar 

  7. J.D. Emerald, K.G. Subramanian and D.G. Thomas. Inferring Subclasses of contextual languages. Proceedings of the 5th International Colloquium ICGI 2000. (A. Oliveira, ed). LNAI Vol. 1891, pp 65–74. 2000.

    Google Scholar 

  8. H. Fernau. Learning of terminal distinguishable languages. Technical Report WSI-99-23. Universität Tübingen (Germany), Wilhelm-Schickard-Institut für Informatik, 1999.

    Google Scholar 

  9. H. Fernau. k-gram extensions of terminal distinguishable languages. Proceedings of the International Conference on Pattern Recognition ICPR 2000, Vol. 2 pp 125–128. IEEE Press. 2000.

    Article  Google Scholar 

  10. H. Fernau. Identification of function distinguishable languages. Proceedings of the 11th International Conference on Algorithmic Learning Theory ALT 2000. (H. Arimura, S. Jain, A. Sharma, eds.). LNCS Vol. 1968 pp 116–130. Springer-Verlag 2000.

    Chapter  Google Scholar 

  11. P. García. Explorabilidad Local en Inferencia Inductiva de Lenguajes Regulares y Aplicaciones. Ph.D. Thesis. Departamento de Sistemas Informáticos y Computación. Universidad Politécnica de Valencia. 1988.

    Google Scholar 

  12. P. García, E. Vidal and J. Oncina. Learning Locally Testable Languages in the Strict Sense. Proceedings of the First International Workshop on Algorithmic Learning Theory. pp 325–338. 1990.

    Google Scholar 

  13. M. Gold. Language Identification in the Limit. Information and Control 10, pp 447–474. 1967.

    Article  MATH  Google Scholar 

  14. J. Hopcroft and J. Ullman. Introduction to Automata Theory, Languages and Computation. Addison-Wesley Publishing Co. 1979.

    Google Scholar 

  15. T. Koshiba, E. Mäkinen, Y. Takada. Learning deterministic even linear languages from positive examples. Theoretical Computer Science 185, pp 63–97. 1997.

    Article  MATH  MathSciNet  Google Scholar 

  16. E. Mäkinen. On inferring zero-reversible languages. Acta Cybernetica 14, pp 479–484. 2000.

    MATH  MathSciNet  Google Scholar 

  17. R. McNaughton and S. Papert. Counter-free automata. MIT Press. 1971.

    Google Scholar 

  18. V. Radhakrishnan. Grammatical Inference from Positive Data: An Efective Integrated Approach. PhD. Thesis. Department of Computer Science and Engineering. IIT, Bombay. 1987.

    Google Scholar 

  19. V. Radhakrishnan and G. Nagaraja. Inference of Regular Grammars via Skeletons. IEEE Trans. on Systems, Man and Cybernetics, 17, No. 6 pp 982–992. 1987.

    MathSciNet  Google Scholar 

  20. V. Radhakrishnan and G. Nagaraja. Inference of Even Linear Languages and Its Application to Picture Description Languages. Pattern Recognition, 21, No. 1. pp 55–62. 1988.

    Article  MATH  MathSciNet  Google Scholar 

  21. J. Ruiz. Familias de Lenguajes Explorables: Inferencia Inductiva y Caracterizaciön Algebraica. Ph.D. Thesis. Departamento de Sistemas Informáticos y Computación. Universidad Politécnica de Valencia. 1997.

    Google Scholar 

  22. Y. Sakakibara. Recent advances of grammatical inference. Theoretical Computer Science 185, pp 15–45. 1997.

    Article  MATH  MathSciNet  Google Scholar 

  23. J.M. Sempere and P. García. A Characterization of Even Linear Languages ans its Application to the Learning Problem. Proceedings of ICGI’94 (R. Carrasco and J. Oncina, eds.). LNAI Vol. 862, pp 38–44. Springer-Verlag. 1994.

    Google Scholar 

  24. Y. Takada. Grammatical Inference of Even Linear Languages based on Control Sets. Information Processing Letters 28, No. 4, pp 193–199. 1988.

    Article  MATH  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. Departamento de Sistemas Informáticos y Computación, Universidad Politécnica de Valencia, Valencia, Spain

    José M. Sempere & Pedro García

Authors
  1. José M. Sempere
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  2. Pedro García
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Editors and Affiliations

  1. Perot Systems Nederland B.V., Hoefseweg 1, 3821 AE, Amersfoort, The Netherlands

    Pieter Adriaans (Senior Research Advisor, Professor of Learning and Adaptive Systems) (Senior Research Advisor, Professor of Learning and Adaptive Systems)

  2. ILLC/Computation and Complexity Theory, Universiteit van Amsterdam, Plantage Muidergracht 24, 1018 TV, Amsterdam, The Netherlands

    Pieter Adriaans (Senior Research Advisor, Professor of Learning and Adaptive Systems) (Senior Research Advisor, Professor of Learning and Adaptive Systems)

  3. School of Electrical Engineering and Computer Science, University of Newcastle, University Drive, Callaghan, NSW, 2308, Australia

    Henning Fernau

  4. Wilhelm-Schickard-Institut für Informatik, Universität Tübingen, Sand 13, 72076, Tübingen, Germany

    Henning Fernau

  5. FNWI/ILLC, Cognitive Systems and Information Processing Group, Universiteit van Amsterdam, Room B-5.39, Nieuwe Achtergracht 166, 1018 WV, Amsterdam, The Netherlands

    Menno van Zaanen

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Sempere, J.M., García, P. (2002). Learning Locally Testable Even Linear Languages from Positive Data. In: Adriaans, P., Fernau, H., van Zaanen, M. (eds) Grammatical Inference: Algorithms and Applications. ICGI 2002. Lecture Notes in Computer Science(), vol 2484. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45790-9_18

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  • DOI: https://doi.org/10.1007/3-540-45790-9_18

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  • Print ISBN: 978-3-540-44239-4

  • Online ISBN: 978-3-540-45790-9

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