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Optimized fixed-size kernel models for large data sets

Author

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  • De Brabanter, K.
  • De Brabanter, J.
  • Suykens, J.A.K.
  • De Moor, B.

Abstract

A modified active subset selection method based on quadratic Rényi entropy and a fast cross-validation for fixed-size least squares support vector machines is proposed for classification and regression with optimized tuning process. The kernel bandwidth of the entropy based selection criterion is optimally determined according to the solve-the-equation plug-in method. Also a fast cross-validation method based on a simple updating scheme is developed. The combination of these two techniques is suitable for handling large scale data sets on standard personal computers. Finally, the performance on test data and computational time of this fixed-size method are compared to those for standard support vector machines and [nu]-support vector machines resulting in sparser models with lower computational cost and comparable accuracy.

Suggested Citation

  • De Brabanter, K. & De Brabanter, J. & Suykens, J.A.K. & De Moor, B., 2010. "Optimized fixed-size kernel models for large data sets," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1484-1504, June.
    • Handle: RePEc:eee:csdana:v:54:y:2010:i:6:p:1484-1504
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    References listed on IDEAS

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    1. Liu, Yufeng & Helen Zhang, Hao & Park, Cheolwoo & Ahn, Jeongyoun, 2007. "Support vector machines with adaptive Lq penalty," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 6380-6394, August.
    2. Sheather, Simon J., 1986. "An improved data-based algorithm for choosing the window width when estimating the density at a point," Computational Statistics & Data Analysis, Elsevier, vol. 4(1), pages 61-65, June.
    3. L. Ingber, 1989. "Very fast simulated re-annealing," Lester Ingber Papers 89vf, Lester Ingber.
    4. Hall, Peter & Wolff, Rodney C. L. & Yao, Qiwei, 1999. "Methods for estimating a conditional distribution function," LSE Research Online Documents on Economics 6631, London School of Economics and Political Science, LSE Library.
    5. Fan, Jianqing & Yao, Qiwei & Tong, Howell, 1996. "Estimation of conditional densities and sensitivity measures in nonlinear dynamical systems," LSE Research Online Documents on Economics 6704, London School of Economics and Political Science, LSE Library.
    6. Qi Li & Jeffrey Scott Racine, 2006. "Nonparametric Econometrics: Theory and Practice," Economics Books, Princeton University Press, edition 1, volume 1, number 8355.
    7. Huang, Chien-Ming & Lee, Yuh-Jye & Lin, Dennis K.J. & Huang, Su-Yun, 2007. "Model selection for support vector machines via uniform design," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 335-346, September.
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    Cited by:

    1. Luts, Jan & Molenberghs, Geert & Verbeke, Geert & Van Huffel, Sabine & Suykens, Johan A.K., 2012. "A mixed effects least squares support vector machine model for classification of longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 56(3), pages 611-628.
    2. De Brabanter, Kris & Suykens, Johan & De Moor, Bart, 2013. "Nonparametric Regression via StatLSSVM," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 55(i02).

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