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Nonlinear support vector machines through iterative majorization and I-splines

Author

Listed:
  • Groenen, P.J.F.
  • Bioch, J.C.
  • Nalbantov, G.I.

Abstract

To minimize the primal support vector machine (SVM) problem, we propose to use iterative majorization. To do so, we propose to use it- erative majorization. To allow for nonlinearity of the predictors, we use (non)monotone spline transformations. An advantage over the usual ker- nel approach in the dual problem is that the variables can be easily inter- preted. We illustrate this with an example from the literature.

Suggested Citation

  • Groenen, P.J.F. & Bioch, J.C. & Nalbantov, G.I., 2006. "Nonlinear support vector machines through iterative majorization and I-splines," Econometric Institute Research Papers EI 2006-25, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    • Handle: RePEc:ems:eureir:7889
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    References listed on IDEAS

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    1. Hunter D.R. & Lange K., 2004. "A Tutorial on MM Algorithms," The American Statistician, American Statistical Association, vol. 58, pages 30-37, February.
    2. Kiers, Henk A. L., 2002. "Setting up alternating least squares and iterative majorization algorithms for solving various matrix optimization problems," Computational Statistics & Data Analysis, Elsevier, vol. 41(1), pages 157-170, November.
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