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Comparing Linear Discriminant Analysis and Support Vector Machines

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Advances in Information Systems (ADVIS 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2457))

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Abstract

Both Linear Discriminant Analysis and Support Vector Machines compute hyperplanes that are optimal with respect to their individual objectives. However, there can be vast differences in performance between the two techniques depending on the extent to which their respective assumptions agree with problems at hand. In this paper we compare the two techniques analytically and experimentally using a number of data sets. For analytical comparison purposes, a unified representation is developed and a metric of optimality is proposed.

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Author information

Authors and Affiliations

  1. Dept. of EECS, Tulane University, 70118, New Orleans, LA

    Ibrahim Gokcen & Jing Peng

Authors
  1. Ibrahim Gokcen
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  2. Jing Peng
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Editor information

Editors and Affiliations

  1. Computer Engineering Department, Dokuz Eylul University, 35100, Izmir, Bornova, Turkey

    Tatyana Yakhno

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© 2002 Springer-Verlag Berlin Heidelberg

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Gokcen, I., Peng, J. (2002). Comparing Linear Discriminant Analysis and Support Vector Machines. In: Yakhno, T. (eds) Advances in Information Systems. ADVIS 2002. Lecture Notes in Computer Science, vol 2457. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36077-8_10

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  • DOI: https://doi.org/10.1007/3-540-36077-8_10

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00009-9

  • Online ISBN: 978-3-540-36077-3

  • eBook Packages: Springer Book Archive

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