期刊
IEEE TRANSACTIONS ON NEURAL NETWORKS
卷 16, 期 2, 页码 498-501出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNN.2004.841785
关键词
conjugate gradient (CG); least square support vector machines (LS-SVM); sequential minimal optimization (SMO)
类别
资金
- NIGMS NIH HHS [1P01 GM63208] Funding Source: Medline
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.
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