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

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

Jan 2008

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2008spie.6937e..69d&link_type=abstract

Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2007. Edited by Romaniuk, Rys

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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.

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