5 August 2009 Structural optimization of least-squares support vector classifier based on virtual leave-one-out residuals
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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.
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Stanislaw Jankowski, Stanislaw Jankowski, Zbigniew Szymański, 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); doi: 10.1117/12.839616; https://doi.org/10.1117/12.839616
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