Convergence proof of a sequential minimal optimization algorithm for support vector regression

Jun Guo; Norikazu Takahashi; Tetsuo Nishi

Convergence proof of a sequential minimal optimization algorithm for support vector regression

Jun Guo, Norikazu Takahashi, Tetsuo Nishi

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

A sequential minimal optimization (SMO) algorithm for support vector regression (SVR) has recently been proposed by Flake and Lawrence. However, the convergence of their algorithm has not been proved so far. In this paper, we consider an SMO algorithm, which deals with the same optimization problem as Flake and Lawrence's SMO, and give a rigorous proof that it always stops within a finite number of iterations.

Original language	English
Title of host publication	International Joint Conference on Neural Networks 2006, IJCNN '06
Pages	355-362
Number of pages	8
Publication status	Published - Dec 1 2006
Externally published	Yes
Event	International Joint Conference on Neural Networks 2006, IJCNN '06 - Vancouver, BC, Canada Duration: Jul 16 2006 → Jul 21 2006

Publication series

Name	IEEE International Conference on Neural Networks - Conference Proceedings
ISSN (Print)	1098-7576

Other

Other	International Joint Conference on Neural Networks 2006, IJCNN '06
Country	Canada
City	Vancouver, BC
Period	7/16/06 → 7/21/06

ASJC Scopus subject areas

Software

Cite this

Convergence proof of a sequential minimal optimization algorithm for support vector regression. / Guo, Jun; Takahashi, Norikazu; Nishi, Tetsuo.

International Joint Conference on Neural Networks 2006, IJCNN '06. 2006. p. 355-362 1716114 (IEEE International Conference on Neural Networks - Conference Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Guo, J, Takahashi, N & Nishi, T 2006, Convergence proof of a sequential minimal optimization algorithm for support vector regression. in International Joint Conference on Neural Networks 2006, IJCNN '06., 1716114, IEEE International Conference on Neural Networks - Conference Proceedings, pp. 355-362, International Joint Conference on Neural Networks 2006, IJCNN '06, Vancouver, BC, Canada, 7/16/06.

@inproceedings{23997c4549a6483f9a586ff1e5f659b9,

title = "Convergence proof of a sequential minimal optimization algorithm for support vector regression",

abstract = "A sequential minimal optimization (SMO) algorithm for support vector regression (SVR) has recently been proposed by Flake and Lawrence. However, the convergence of their algorithm has not been proved so far. In this paper, we consider an SMO algorithm, which deals with the same optimization problem as Flake and Lawrence's SMO, and give a rigorous proof that it always stops within a finite number of iterations.",

author = "Jun Guo and Norikazu Takahashi and Tetsuo Nishi",

year = "2006",

month = dec,

day = "1",

language = "English",

isbn = "0780394909",

series = "IEEE International Conference on Neural Networks - Conference Proceedings",

pages = "355--362",

booktitle = "International Joint Conference on Neural Networks 2006, IJCNN '06",

note = "International Joint Conference on Neural Networks 2006, IJCNN '06 ; Conference date: 16-07-2006 Through 21-07-2006",

}

TY - GEN

T1 - Convergence proof of a sequential minimal optimization algorithm for support vector regression

AU - Guo, Jun

AU - Takahashi, Norikazu

AU - Nishi, Tetsuo

PY - 2006/12/1

Y1 - 2006/12/1

N2 - A sequential minimal optimization (SMO) algorithm for support vector regression (SVR) has recently been proposed by Flake and Lawrence. However, the convergence of their algorithm has not been proved so far. In this paper, we consider an SMO algorithm, which deals with the same optimization problem as Flake and Lawrence's SMO, and give a rigorous proof that it always stops within a finite number of iterations.

AB - A sequential minimal optimization (SMO) algorithm for support vector regression (SVR) has recently been proposed by Flake and Lawrence. However, the convergence of their algorithm has not been proved so far. In this paper, we consider an SMO algorithm, which deals with the same optimization problem as Flake and Lawrence's SMO, and give a rigorous proof that it always stops within a finite number of iterations.

UR - http://www.scopus.com/inward/record.url?scp=40649090457&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=40649090457&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:40649090457

SN - 0780394909

SN - 9780780394902

T3 - IEEE International Conference on Neural Networks - Conference Proceedings

SP - 355

EP - 362

BT - International Joint Conference on Neural Networks 2006, IJCNN '06

T2 - International Joint Conference on Neural Networks 2006, IJCNN '06

Y2 - 16 July 2006 through 21 July 2006

ER -