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Hybrid Weighted Distance Measures and Their Application to Pattern Recognition

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Book cover Intelligent Data Engineering and Automated Learning – IDEAL 2008 (IDEAL 2008)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 5326))

Abstract

Distance measures are an important means to find the difference of data. In this paper, we develop a type of hybrid weighted distance measures which are based on the weighted distance measures and the ordered weighted averaging operator, and aslo point out some of their special cases. Then, we apply the developed measures to pattern recognition.

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Author information

Authors and Affiliations

  1. School of Economics and Management, Southeast University, Nanjing, 210096, China

    Zeshui Xu

Authors
  1. Zeshui Xu
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Editor information

Editors and Affiliations

  1. University of West Scotland, PA1 2BE, Paisley, Scotland

    Colin Fyfe

  2. KAIST, Daejeon, Korea

    Dongsup Kim

  3. Brain Science Research Center and Department of Bio & Brain Engineering, Korea Advanced Institute of Science and Technology, 373-1 Guseong-dong, Yuseong-gu, 305-701, Daejeon, Korea

    Soo-Young Lee

  4. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

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© 2008 Springer-Verlag Berlin Heidelberg

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Xu, Z. (2008). Hybrid Weighted Distance Measures and Their Application to Pattern Recognition. In: Fyfe, C., Kim, D., Lee, SY., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2008. IDEAL 2008. Lecture Notes in Computer Science, vol 5326. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88906-9_3

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  • DOI: https://doi.org/10.1007/978-3-540-88906-9_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-88905-2

  • Online ISBN: 978-3-540-88906-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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