Structural optimization of least-squares support vector classifier based on virtual leave-one-out residuals
- Stanisław Jankowski,
- Zbigniew Szymański
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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- Romaniuk Ryszard, Ryszard Romaniuk Kulpa Krzysztof Krzysztof Kulpa (eds.): Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2009, vol. 7502, 2009, SPIE, Bellingham, SPIE, 786 p., ISBN 9780819478139. DOI:10.1117/12.843290 Opening in a new tab
- DOI:10.1117/12.839616 Opening in a new tab
- http://dx.doi.org/10.1117/12.839616 Opening in a new tab
- (en) English
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