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A nonlinear discriminant algorithm for data projection and feature extraction

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Artificial Neural Networks — ICANN 96 (ICANN 1996)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1112))

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Abstract

A nonlinear supervised feature extraction algorithm that directly combines Fisher's criterion function with a preliminary non linear projection of vectors in pattern space will be described. After some computational details are given, a comparison with Fisher's linear method will be made over a concrete example.

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References

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Authors and Affiliations

  1. Department of Computer Engineering and Instituto de Ingeniería del Conocimiento, Universidad Autónoma de Madrid, 28049, Madrid, Spain

    C. Santa Cruz & J. Dorronsoro

Authors
  1. C. Santa Cruz
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  2. J. Dorronsoro
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Editor information

Christoph von der MalsburgWerner von SeelenJan C. VorbrüggenBernhard Sendhoff

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© 1996 Springer-Verlag Berlin Heidelberg

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Santa Cruz, C., Dorronsoro, J. (1996). A nonlinear discriminant algorithm for data projection and feature extraction. In: von der Malsburg, C., von Seelen, W., Vorbrüggen, J.C., Sendhoff, B. (eds) Artificial Neural Networks — ICANN 96. ICANN 1996. Lecture Notes in Computer Science, vol 1112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61510-5_96

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  • DOI: https://doi.org/10.1007/3-540-61510-5_96

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61510-1

  • Online ISBN: 978-3-540-68684-2

  • eBook Packages: Springer Book Archive

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