Abstract
Sequential minimal optimization (SMO) algorithm is one of the simplest decomposition methods for learning of support vector machines (SVMs). Keerthi and Gilbert have recently studied the convergence property of SMO algorithm and given a proof that SMO algorithm always stops within a finite number of iterations. In this letter, we point out the incompleteness of their proof and give a more rigorous proof.
| Original language | English |
|---|---|
| Pages (from-to) | 774-776 |
| Number of pages | 3 |
| Journal | IEEE Transactions on Neural Networks |
| Volume | 16 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - May 2005 |
| Externally published | Yes |
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Keywords
- Convergence
- Sequential minimal optimization (SMO) algorithm
- Support vector machines (SVMs)
- Termination
ASJC Scopus subject areas
- Control and Systems Engineering
- Theoretical Computer Science
- Electrical and Electronic Engineering
- Artificial Intelligence
- Computational Theory and Mathematics
- Hardware and Architecture
Cite this
Rigorous proof of termination of SMO algorithm for support vector machines. / Takahashi, Norikazu; Nishi, Tetsuo.
In: IEEE Transactions on Neural Networks, Vol. 16, No. 3, 05.2005, p. 774-776.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Rigorous proof of termination of SMO algorithm for support vector machines
AU - Takahashi, Norikazu
AU - Nishi, Tetsuo
PY - 2005/5
Y1 - 2005/5
N2 - Sequential minimal optimization (SMO) algorithm is one of the simplest decomposition methods for learning of support vector machines (SVMs). Keerthi and Gilbert have recently studied the convergence property of SMO algorithm and given a proof that SMO algorithm always stops within a finite number of iterations. In this letter, we point out the incompleteness of their proof and give a more rigorous proof.
AB - Sequential minimal optimization (SMO) algorithm is one of the simplest decomposition methods for learning of support vector machines (SVMs). Keerthi and Gilbert have recently studied the convergence property of SMO algorithm and given a proof that SMO algorithm always stops within a finite number of iterations. In this letter, we point out the incompleteness of their proof and give a more rigorous proof.
KW - Convergence
KW - Sequential minimal optimization (SMO) algorithm
KW - Support vector machines (SVMs)
KW - Termination
UR - http://www.scopus.com/inward/record.url?scp=19344375172&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=19344375172&partnerID=8YFLogxK
U2 - 10.1109/TNN.2005.844857
DO - 10.1109/TNN.2005.844857
M3 - Article
C2 - 15941003
AN - SCOPUS:19344375172
VL - 16
SP - 774
EP - 776
JO - IEEE Transactions on Neural Networks and Learning Systems
JF - IEEE Transactions on Neural Networks and Learning Systems
SN - 2162-237X
IS - 3
ER -