Global convergence of SMO algorithm for support vector regression

Norikazu Takahashi; Jun Guo; Tetsuo Nishi

doi:10.1109/TNN.2007.915116

Global convergence of SMO algorithm for support vector regression

Norikazu Takahashi, Jun Guo, Tetsuo Nishi

Research output: Contribution to journal › Article › peer-review

35 Citations (Scopus)

Abstract

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.

Original language	English
Pages (from-to)	971-982
Number of pages	12
Journal	IEEE Transactions on Neural Networks
Volume	19
Issue number	6
DOIs	https://doi.org/10.1109/TNN.2007.915116
Publication status	Published - Dec 1 2008
Externally published	Yes

Keywords

Convergence
Quadratic programming (QP)
Sequential minimal optimization (SMO)
Support vector regression (SVR)

ASJC Scopus subject areas

Software
Computer Science Applications
Computer Networks and Communications
Artificial Intelligence

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title = "Global convergence of SMO algorithm for support vector regression",

abstract = "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.",

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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

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KW - Sequential minimal optimization (SMO)

KW - Support vector regression (SVR)

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