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
Guo, J., Takahashi, N., & Nishi, T. (2006). Convergence proof of a sequential minimal optimization algorithm for support vector regression. In IEEE International Conference on Neural Networks - Conference Proceedings (pp. 355-362). [1716114]