DocumentCode :
1246101
Title :
An improved conjugate gradient scheme to the solution of least squares SVM
Author :
Chu, Wei ; Ong, Chong Jin ; Keerthi, S. Sathiya
Author_Institution :
Univ. Coll. London, UK
Volume :
16
Issue :
2
fYear :
2005
fDate :
3/1/2005 12:00:00 AM
Firstpage :
498
Lastpage :
501
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.
Keywords :
conjugate gradient methods; least squares approximations; minimisation; reduced order systems; support vector machines; conjugate gradient scheme; least square support vector machine; linear equation; reduced system; sequential minimal optimization; Character generation; Equations; Genomics; Iterative algorithms; Kernel; Least squares approximation; Least squares methods; Linear systems; Pattern recognition; Support vector machines; Conjugate gradient (CG); least square support vector machines (LS-SVM); sequential minimal optimization (SMO); Least-Squares Analysis;
fLanguage :
English
Journal_Title :
Neural Networks, IEEE Transactions on
Publisher :
ieee
ISSN :
1045-9227
Type :
jour
DOI :
10.1109/TNN.2004.841785
Filename :
1402511
Link To Document :
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