1. A fast conjugate functional gain sequential minimal optimization training algorithm for LS-SVM model.
- Author
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Yu, Lang, Ma, Xin, and Li, Shengjie
- Subjects
MACHINE learning ,MATHEMATICAL optimization ,SUPPORT vector machines ,IMAGE encryption - Abstract
The least squares support vector machine (LS-SVM) is an effective method to deal with classification and regression problems and has been widely studied and applied in the fields of machine learning and pattern recognition. The learning algorithms of the LS-SVM are usually conjugate gradient (CG) and sequential minimal optimization (SMO) algorithms. Based on this, we propose a conjugate functional gain SMO algorithm and theoretically prove its asymptotic convergence. This algorithm combines the conjugate direction method and the functional gain SMO algorithm with second-order information, which increases the functional gain of the plain SMO algorithm. In addition, we also provide a generalized SMO-type algorithm framework with a simple iterative format and easy implementation for other LS-SVM training algorithms. The numerical results show that the execution time of this algorithm is significantly shorter than that of the other plain SMO-type algorithms and CG-type algorithms. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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