Please use this identifier to cite or link to this item: https://doi.org/10.1109/TNN.2004.841785
DC FieldValue
dc.titleAn improved conjugate gradient scheme to the solution of least squares SVM
dc.contributor.authorChu, W.
dc.contributor.authorOng, C.J.
dc.contributor.authorKeerthi, S.S.
dc.date.accessioned2014-06-17T06:11:51Z
dc.date.available2014-06-17T06:11:51Z
dc.date.issued2005-03
dc.identifier.citationChu, 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
dc.identifier.issn10459227
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/59472
dc.description.abstractThe 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.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/TNN.2004.841785
dc.sourceScopus
dc.subjectConjugate gradient (CG)
dc.subjectLeast square support vector machines (LS-SVM)
dc.subjectSequential minimal optimization (SMO)
dc.typeArticle
dc.contributor.departmentMECHANICAL ENGINEERING
dc.description.doi10.1109/TNN.2004.841785
dc.description.sourcetitleIEEE Transactions on Neural Networks
dc.description.volume16
dc.description.issue2
dc.description.page498-501
dc.description.codenITNNE
dc.identifier.isiut000227407500021
Appears in Collections:Staff Publications

Show simple item record
Files in This Item:
There are no files associated with this item.

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.