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Support vector machines for multi-class classification

  • Engeneering Applications
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Engineering Applications of Bio-Inspired Artificial Neural Networks (IWANN 1999)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1607))

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Abstract

Support vector machines (SVMs) are primarily designed for 2-class classification problems. Although in several papers it is mentioned that the combination of K SVMs can be used to solve a K-class classification problem, such a procedure requires some care. In this paper, the scaling problem of different SVMs is highlighted. Various normalization methods are proposed to cope with this problem and their efficiencies are measured empirically. This simple way of ssing SVMs to learn a K-class classification problem consists in choosing the maximum applied to the outputs of K SVMs solving a one-per-class decomposition of the general problem. In the second part of this paper, more sophisticated techniques are suggested. On the one hand, a stacking of the K SVMs with other classification techniques is proposed. On the other end, the one-per-class decomposition scheme is replaced by more elaborated schemes based on error-correcting codes. An incremental algorithm for the elaboration of pertinent decomposition schemes is mentioned, which exploits the properties of SVMs for an efficient computation.

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Author information

Authors and Affiliations

  1. IDIAP-Dalle Molle Institute for Perceptual Artificial Intelligence, CP 592, CH-1920, Martigny, Switzerland

    Eddy Mayoraz & Ethem Alpaydin

  2. Dept of Computer Engineering, Bogazici University, TR-80815, Istanbul, Turkey

    Eddy Mayoraz & Ethem Alpaydin

Authors
  1. Eddy Mayoraz
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  2. Ethem Alpaydin
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Editor information

José Mira Juan V. Sánchez-Andrés

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© 1999 Springer-Verlag Berlin Heidelberg

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Mayoraz, E., Alpaydin, E. (1999). Support vector machines for multi-class classification. In: Mira, J., Sánchez-Andrés, J.V. (eds) Engineering Applications of Bio-Inspired Artificial Neural Networks. IWANN 1999. Lecture Notes in Computer Science, vol 1607. Springer, Berlin, Heidelberg . https://doi.org/10.1007/BFb0100551

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  • DOI: https://doi.org/10.1007/BFb0100551

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66068-2

  • Online ISBN: 978-3-540-48772-2

  • eBook Packages: Springer Book Archive

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