Working Set Selection Using Functional Gain for LS-SVM

Author

Bo, Liefeng ; Jiao, Licheng ; Wang, Ling

Author_Institution

Xi- dian Univ., Xi´´an

Volume

18

Issue

5

fYear

2007

Firstpage

1541

Lastpage

1544

Abstract

The efficiency of sequential minimal optimization (SMO) depends strongly on the working set selection. This letter shows how the improvement of SMO in each iteration, named the functional gain (FG), is used to select the working set for least squares support vector machine (LS-SVM). We prove the convergence of the proposed method and give some theoretical support for its performance. Empirical comparisons demonstrate that our method is superior to the maximum violating pair (MVP) working set selection.

Keywords

convergence of numerical methods; iterative methods; least squares approximations; optimisation; set theory; support vector machines; LS-SVM; convergence; functional gain; least squares support vector machine; sequential minimal optimization; working set selection; Character generation; Convergence; Fasteners; Gaussian processes; Kernel; Large-scale systems; Least squares methods; Quadratic programming; Support vector machine classification; Support vector machines; Fast algorithm; least squares support vector machine (LS-SVM); sequential minimal optimization (SMO); Algorithms; Artificial Intelligence; Computer Simulation; Models, Theoretical; Pattern Recognition, Automated;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/TNN.2007.899715

Filename

4298104