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Topographic Map Object Classification Using Real-Value Grammar Classifier System

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Perception and Machine Intelligence (PerMIn 2012)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7143))

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Abstract

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.

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References

  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), http://computer.org/tpami/tp2002/i0509abs.htm

    Article  Google Scholar 

  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. Cielecki, L., Unold, O.: Real-valued gcs classifier system. International Journal of Applied Mathematics and Computer Science 17(4), 539–547 (2007)

    Article  Google Scholar 

  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. 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. 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)

    Chapter  Google Scholar 

  7. Gold, E.: Language identification in the limit. Information and Control 10(5), 447–474 (1967)

    Article  MathSciNet  MATH  Google Scholar 

  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), http://doi.acm.org/10.1145/1656274.1656278

    Article  Google Scholar 

  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. Holland, J.H.: Adaptation. In: Rosen, R., Snell, F. (eds.) Progress in Theoretical Biology. Academic Press (1976)

    Google Scholar 

  11. Kohonen, T.: Self–organizing formation of topologically correct feature maps. Biological Cybernetics 43, 59–69 (1982)

    Article  MathSciNet  MATH  Google Scholar 

  12. Unold, O.: Context-free grammar induction with grammar-based classifier system. Archives of Control Science 15(4), 681 (2005)

    MATH  Google Scholar 

  13. Unold, O., Cielecki, L.: Grammar-based classifier system. Issues in Intelligent Systems: Paradigms, 273–286 (2005)

    Google Scholar 

  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)

    Chapter  Google Scholar 

  15. Wilson, S.: Get real! xcs with continuous-valued inputs. Learning Classifier Systems, 209–219 (2000)

    Google Scholar 

  16. Younger, D.: Recognition and parsing of context-free languages in time n3*. Information and Control 10(2), 189–208 (1967)

    Article  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Institute of Computer Engineering, Control and Robotics, Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland

    Lukasz Cielecki

Authors
  1. Lukasz Cielecki
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Editor information

Editors and Affiliations

  1. Indian Statistical Institute (ISI), Machine Intelligence Unit, Kolkata, India

    Malay K. Kundu  & Sushmita Mitra  & 

  2. Centre for Development of Advanced Computing (C-DAC), Kolkata, India

    Debasis Mazumdar

  3. Indian Statistical Instituten (ISI), Kolkata, India

    Sankar K. Pal

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Cielecki, L. (2012). Topographic Map Object Classification Using Real-Value Grammar Classifier System. In: Kundu, M.K., Mitra, S., Mazumdar, D., Pal, S.K. (eds) Perception and Machine Intelligence. PerMIn 2012. Lecture Notes in Computer Science, vol 7143. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27387-2_27

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  • DOI: https://doi.org/10.1007/978-3-642-27387-2_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-27386-5

  • Online ISBN: 978-3-642-27387-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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