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International Conference on Computational Science and Its Applications

ICCSA 2006: Computational Science and Its Applications - ICCSA 2006 pp 1210–1215Cite as

The Optimal Feature Extraction Procedure for Statistical Pattern Recognition

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3982))

Abstract

The paper deals with the extraction of features for object recognition. Bayes’ probability of correct classification was adopted as the extraction criterion. The problem with full probabilistic information is discussed in detail. A simple calculation example is given and solved. One of the paper’s chapters is devoted to a case when the available information is contained in the so-called learning sequence (the case of recognition with learning).

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References

  1. Devroye, L., Gyorfi, P., Lugossi, G.: A Probabilistic Theory of Pattern Recognition. Springer, New York (1996)

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  2. Duda, R., Hart, P., Stork, D.: Pattern Classification. Wiley Interscience, New York (2001)

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  4. Guyon, I., Gunn, S., Nikravesh, M., Zadeh, L.: Feature Extraction, Foundations and Applications. Springer, Heidelberg (2004)

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  5. Park, H., Park, C., Pardalos, P.: Comparitive Study of Linear and Nonlinear Feature Extraction Methods - Technical Report, Minneapolis (2004)

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  6. Fukunaga, K.: Introduciton to Statistical Pattern Recognition. Academic Press, London (1990)

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  7. Goldberg, D.: Genetic Algorithms in Search, Optimization and Machine Learning. Adison-Wesley, New York (1989)

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Author information

Authors and Affiliations

  1. Faculty of Electronics, Chair of Systems and Computer Networks, Wroclaw University of Technology, Wyb. Wyspianskiego 27, 50-370, Wroclaw, Poland

    Marek Kurzynski & Edward Puchala

Authors
  1. Marek Kurzynski
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  2. Edward Puchala
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Calgary, 2500 University Drive N.W., T2N 1N4, Calgary, AB, Canada

    Marina Gavrilova

  2. Department of Mathematics and Computer Science, University of Perugia, via Vanvitelli, 1, I-06123, Perugia, Italy

    Osvaldo Gervasi

  3. William Norris Professor, Head of the Computer Science and Engineering Department, University of Minnesota, USA

    Vipin Kumar

  4. OptimaNumerics Ltd., Cathedral House, 23-31 Waring Street, BT1 2DX, Belfast, UK

    C. J. Kenneth Tan

  5. Clayton School of IT, Monash University, 3800, Clayton, Australia

    David Taniar

  6. Department of Chemistry, University of Perugia, Via Elce di Sotto, 8, I-06123, Perugia, Italy

    Antonio Laganá

  7. School of Computing, Soongsil University, Seoul, Korea

    Youngsong Mun

  8. School of Information and Communication Engineering, Sungkyunkwan University, Korea

    Hyunseung Choo

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© 2006 Springer-Verlag Berlin Heidelberg

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Cite this paper

Kurzynski, M., Puchala, E. (2006). The Optimal Feature Extraction Procedure for Statistical Pattern Recognition. In: Gavrilova, M., et al. Computational Science and Its Applications - ICCSA 2006. ICCSA 2006. Lecture Notes in Computer Science, vol 3982. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11751595_127

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  • DOI: https://doi.org/10.1007/11751595_127

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34075-1

  • Online ISBN: 978-3-540-34076-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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