Structural optimization of least-squares support vector classifier based on virtual leave-one-out residuals.

Stanislaw Jankowski; Zbigniew Szymański

doi:10.1117/12.839616

5 August 2009 Structural optimization of least-squares support vector classifier based on virtual leave-one-out residuals

Stanislaw Jankowski, Zbigniew Szymański

Author Affiliations +

Proceedings Volume 7502, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2009; 75021O (2009) https://doi.org/10.1117/12.839616
Event: Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2009, 2009, Wilga, Poland

Abstract

The paper includes description of a novel method of the structural optimization of least squares support vector classifier. The virtual leave-one-out residuals are applied as the criterion for selection of the most influential data. The analytic form of the solution enables to obtain a high gain of the computational cost. The presented method eliminates the drawback of the LS-SVM classifiers - lack of sparseness in the solution. The quality of the method was tested on the artificial data sets - two moons problem and Ripley data set.

Citation Download Citation

Stanislaw Jankowski and Zbigniew Szymański "Structural optimization of least-squares support vector classifier based on virtual leave-one-out residuals", Proc. SPIE 7502, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2009, 75021O (5 August 2009); https://doi.org/10.1117/12.839616

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