TY - JOUR
T1 - Global convergence of SMO algorithm for support vector regression
AU - Takahashi, Norikazu
AU - Guo, Jun
AU - Nishi, Tetsuo
N1 - Funding Information:
Manuscript received November 18, 2006; revised July 27, 2007 and October 12, 2007; accepted October 23, 2007. This work was presented in part at the International Joint Conference on Neural Networks, Vancouver, BC, Canada, July16–21, 2006. This work was supported in part by an Okawa Foundation research grant, the Ministry of Education, Culture, Sports, Science and Technology, Grant-in-Aid for Japan Society for the Promotion of Science (JSPS) Research Fellows 18-9473, and the 21st Century Center of Excellence (COE) Program “Reconstruction of Social Infrastructure Related to Information Science and Electrical Engineering.” N. Takahashi and J. Guo are with the Department of Computer Science and Communication Engineering, Kyushu University, Fukuoka 819-0395, Japan (e-mail: norikazu@csce.kyushu-u.ac.jp; guojun@kairo.csce.kyushu-u.ac.jp).
PY - 2008
Y1 - 2008
N2 - Global convergence of the sequential minimal optimization (SMO) algorithm for support vector regression (SVR) is studied in this paper. Given ι training samples, SVR is formulated as a convex quadratic programming (QP) problem with ι pairs of variables. We prove that if two pairs of variables violating the optimality condition are chosen for update in each step and subproblems are solved in a certain way, then the SMO algorithm always stops within a finite number of iterations after finding an optimal solution. Also, efficient implementation techniques for the SMO algorithm are presented and compared experimentally with other SMO algorithms.
AB - Global convergence of the sequential minimal optimization (SMO) algorithm for support vector regression (SVR) is studied in this paper. Given ι training samples, SVR is formulated as a convex quadratic programming (QP) problem with ι pairs of variables. We prove that if two pairs of variables violating the optimality condition are chosen for update in each step and subproblems are solved in a certain way, then the SMO algorithm always stops within a finite number of iterations after finding an optimal solution. Also, efficient implementation techniques for the SMO algorithm are presented and compared experimentally with other SMO algorithms.
KW - Convergence
KW - Quadratic programming (QP)
KW - Sequential minimal optimization (SMO)
KW - Support vector regression (SVR)
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U2 - 10.1109/TNN.2007.915116
DO - 10.1109/TNN.2007.915116
M3 - Article
C2 - 18541498
AN - SCOPUS:49149114060
VL - 19
SP - 971
EP - 982
JO - IEEE Transactions on Neural Networks and Learning Systems
JF - IEEE Transactions on Neural Networks and Learning Systems
SN - 2162-237X
IS - 6
ER -