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Variants and Performances of Novel Direct Learning Algorithms for L2 Support Vector Machines

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Artificial Intelligence and Soft Computing (ICAISC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8468))

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Abstract

The paper introduces a novel Direct L2 Support Vector Machine (DL2 SVM) classifier and presents the performances of its 4 variants on 12 different binary and multiclass datasets. Direct L2 SVM avoids solving quadratic programming (QP) problem and it solves the Nonnegative Least Squares (NNLS) task instead, which, unlike the related iterative algorithms, produces an impeccably accurate results. Solutions obtained by NNLS and QP are equal but NNLS needs much less CPU time. The comprehensive DL2 SVM model, as well as its three variants, are devised. The similarities with, and differences in respect to, LS SVM and proximal SVMs are pointed at too. The four DL2 SVM models performances are compared in terms of accuracy, percentage of support vectors and CPU time. A strict nested cross-validation (double resampling) is used in all experiments.

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Author information

Authors and Affiliations

  1. Virginia Commonwealth University, 401 West Main Street, Richmond, VA, 23284, USA

    Ljiljana Zigic & Vojislav Kecman

Authors
  1. Ljiljana Zigic
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  2. Vojislav Kecman
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Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Armii Krajowej 36, 42-200, Częstochowa, Poland

    Leszek Rutkowski  & Rafał Scherer  & 

  2. Częstochowa University of Technology, 42-200, Częstochowa, Poland

    Marcin Korytkowski

  3. AGH University of Science and Technology, Mickiewicza 30, 30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  4. Computer Science Division, Department of Electrical Engineering and Computer Sciences, University of California Berkeley, 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

  5. Computational Intelligence Laboratory, Electrical and Computer Engineering, University of Louisville, 405 Lutz Hall, 40292, Louisville, KY, USA

    Jacek M. Zurada

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© 2014 Springer International Publishing Switzerland

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Zigic, L., Kecman, V. (2014). Variants and Performances of Novel Direct Learning Algorithms for L2 Support Vector Machines. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2014. Lecture Notes in Computer Science(), vol 8468. Springer, Cham. https://doi.org/10.1007/978-3-319-07176-3_8

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  • DOI: https://doi.org/10.1007/978-3-319-07176-3_8

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07175-6

  • Online ISBN: 978-3-319-07176-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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