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
fDate :
3/1/2005 12:00:00 AM
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;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2004.841785