Topographic Map Object Classification Using Real-Value Grammar Classifier System

  • Lukasz Cielecki
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7143)


Learning Classifier Systems (LCS) became a large branch of machine learning applications that received a lot of attention recently. Our model of LCS - rGCS or real-value Grammar Classifier System - uses grammar inference to classify real-value vectors which may describe range variety of problems. In this paper we utilize the rGCS core in an object recognition task. Our application seeks for certain graphic symbols on a topographic map scan.


Input Space Context Free Grammar Object Recognition Task Terminal Symbol Kohonen Neural Network 
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.


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  1. 1.
    Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Trans. Pattern Anal. Mach. Intell. 24(4), 509–522 (2002), CrossRefGoogle Scholar
  2. 2.
    Butz, M.: Kernel-based, ellipsoidal conditions in the real-valued xcs classifier system. In: Proceedings of the 2005 Conference on Genetic and Evolutionary Computation, pp. 1835–1842. ACM (2005)Google Scholar
  3. 3.
    Cielecki, L., Unold, O.: Real-valued gcs classifier system. International Journal of Applied Mathematics and Computer Science 17(4), 539–547 (2007)CrossRefGoogle Scholar
  4. 4.
    Cielecki, L., Unold, O.: 3d function approximation with rgcs classifier system. In: Eighth International Conference on Intelligent Systems Design and Applications, ISDA 2008, vol. 3, pp. 136–141. IEEE (2008)Google Scholar
  5. 5.
    Cielecki, L., Unold, O.: Modified himmelblau function classification with rgcs system. In: Eighth International Conference on Hybrid Intelligent Systems, HIS 2008, pp. 879–884. IEEE (2008)Google Scholar
  6. 6.
    Cielecki, L., Unold, O.: GCS with Real-Valued Input. In: Mira, J., Álvarez, J.R. (eds.) IWINAC 2007. LNCS, vol. 4527, pp. 488–497. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  7. 7.
    Gold, E.: Language identification in the limit. Information and Control 10(5), 447–474 (1967)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explorations 11(1), 10–18 (2009), CrossRefGoogle Scholar
  9. 9.
    Holland, J.: Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence. U Michigan Press (1975)Google Scholar
  10. 10.
    Holland, J.H.: Adaptation. In: Rosen, R., Snell, F. (eds.) Progress in Theoretical Biology. Academic Press (1976)Google Scholar
  11. 11.
    Kohonen, T.: Self–organizing formation of topologically correct feature maps. Biological Cybernetics 43, 59–69 (1982)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Unold, O.: Context-free grammar induction with grammar-based classifier system. Archives of Control Science 15(4), 681 (2005)zbMATHGoogle Scholar
  13. 13.
    Unold, O., Cielecki, L.: Grammar-based classifier system. Issues in Intelligent Systems: Paradigms, 273–286 (2005)Google Scholar
  14. 14.
    Unold, O.: Playing a Toy-Grammar with GCS. In: Mira, J., Álvarez, J.R. (eds.) IWINAC 2005. LNCS, vol. 3562, pp. 300–309. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  15. 15.
    Wilson, S.: Get real! xcs with continuous-valued inputs. Learning Classifier Systems, 209–219 (2000)Google Scholar
  16. 16.
    Younger, D.: Recognition and parsing of context-free languages in time n3*. Information and Control 10(2), 189–208 (1967)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Lukasz Cielecki
    • 1
  1. 1.Institute of Computer Engineering, Control and RoboticsWroclaw University of TechnologyWroclawPoland

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