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 | IEEE International Conference on Neural Networks - Conference Proceedings |
| Pages | 355-362 |
| Number of pages | 8 |
| Publication status | Published - 2006 |
| Externally published | Yes |
| Event | International Joint Conference on Neural Networks 2006, IJCNN '06 - Vancouver, BC, Canada Duration: Jul 16 2006 → Jul 21 2006 |
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.
IEEE International Conference on Neural Networks - Conference Proceedings. 2006. p. 355-362 1716114.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
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
Y1 - 2006
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
SN - 0780394909
SN - 9780780394902
SP - 355
EP - 362
BT - IEEE International Conference on Neural Networks - Conference Proceedings
ER -