Please use this identifier to cite or link to this item: https://doi.org/10.1109/TNN.2004.841785
Title: An improved conjugate gradient scheme to the solution of least squares SVM
Authors: Chu, W.
Ong, C.J. 
Keerthi, S.S.
Keywords: Conjugate gradient (CG)
Least square support vector machines (LS-SVM)
Sequential minimal optimization (SMO)
Issue Date: Mar-2005
Source: Chu, W., Ong, C.J., Keerthi, S.S. (2005-03). An improved conjugate gradient scheme to the solution of least squares SVM. IEEE Transactions on Neural Networks 16 (2) : 498-501. ScholarBank@NUS Repository. https://doi.org/10.1109/TNN.2004.841785
Abstract: The least square support vector machines (LS-SVM) formulation corresponds to the solution of a linear system of equations. Several approaches to its numerical solutions have been proposed in the literature. In this letter, we propose an improved method to the numerical solution of LS-SVM and show that the problem can be solved using one reduced system of linear equations. Compared with the existing algorithm for LS-SVM, the approach used in this letter is about twice as efficient. Numerical results using the proposed method are provided for comparisons with other existing algorithms. © 2005 IEEE.
Source Title: IEEE Transactions on Neural Networks
URI: http://scholarbank.nus.edu.sg/handle/10635/59472
ISSN: 10459227
DOI: 10.1109/TNN.2004.841785
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