Selection of significant samples to reduce the complexity of least-squares support vector machine

Giuseppe Di Salvo; Stanisław Jankowski; Ewa Piątkowska-Janko; Paolo Arena

doi:10.1117/12.784710

28 December 2007 Selection of significant samples to reduce the complexity of least-squares support vector machine

Giuseppe Di Salvo, Stanisław Jankowski, Ewa Piątkowska-Janko, Paolo Arena

Proceedings Volume 6937, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2007; 69371X (2007) https://doi.org/10.1117/12.784710
Event: Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2007, 2007, Wilga, Poland

Abstract

The least-squares support vector machines (LS-SVM) can be obtained by solving a simpler optimization problem than that in standard support vector machines (SVM). Its shortcoming is the loss of sparseness and this usually results in slow testing speed. Several pruning methods have been proposed to improve the sparseness of a LS-SVM trained on the whole training dataset. A selection of significative samples is proposed to train a LS-SVM on a reduced dataset. A dataset about electrocardiogram (ECG) of 376 patients has been used to assess the proposed algorithm.

Citation Download Citation

Giuseppe Di Salvo, Stanisław Jankowski, Ewa Piątkowska-Janko, and Paolo Arena "Selection of significant samples to reduce the complexity of least-squares support vector machine", Proc. SPIE 6937, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2007, 69371X (28 December 2007); https://doi.org/10.1117/12.784710

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