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Computing Convex-Layers by a Multi-layer Self-organizing Neural Network

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Neural Information Processing (ICONIP 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3316))

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Abstract

A multi-layer self-organizing neural network model has been proposed for computation of the convex-layers of a given set of planar points. Computation of convex-layers has been found to be useful in pattern recognition and in statistics. The proposed network architecture evolves in such a manner that it adapts itself to the hull-vertices of the convex-layers in the required order. Time complexity of the proposed model is also discussed.

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Author information

Authors and Affiliations

  1. Indian Statistical Institute, 203 B. T. Road, Calcutta, 700 108

    Amitava Datta & Srimanta Pal

Authors
  1. Amitava Datta
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  2. Srimanta Pal
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Editor information

Editors and Affiliations

  1. Indian Statistical Institute, Electronics and Communication Sciences Unit, Kolkata, India

    Nikhil Ranjan Pal

  2. School of Computer and Information Sciences, Knowledge Engineering and Discovery Research Institute (KEDRI), Auckland University of Technology, Private Bag 92006, Auckland, New Zealand

    Nik Kasabov

  3. Department of Instrumentation and Electronics Engineering, Jadavpur University, Salt-lake Campus, 700098, Calcutta, India

    Rajani K. Mudi

  4. Indian Statistical Institute, 203 B. T. Road, 700 108, Calcutta,  

    Srimanta Pal

  5. Indian Statistical Institute, Computer Vision and Pattern Recognition Unit, 700108, Kolkata, India

    Swapan Kumar Parui

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© 2004 Springer-Verlag Berlin Heidelberg

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Datta, A., Pal, S. (2004). Computing Convex-Layers by a Multi-layer Self-organizing Neural Network. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds) Neural Information Processing. ICONIP 2004. Lecture Notes in Computer Science, vol 3316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30499-9_99

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  • DOI: https://doi.org/10.1007/978-3-540-30499-9_99

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23931-4

  • Online ISBN: 978-3-540-30499-9

  • eBook Packages: Springer Book Archive

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