Abstract
A support vector machine (SVM) has been developed for two-class problems, although its application to multiclass problems is not straightforward. This paper proposes a new Lagrangian SVM (LSVM) for application to multiclass problems. The multiclass Lagrangian SVM is formulated as a single optimization problem considering all the classes together, and a training method tailored to the multiclass problem is presented. A multiclass output representation matrix is defined to simplify the optimization formulation and associated training method. The proposed method is applied to some benchmark datasets in repository, and its effectiveness is demonstrated via simulation.
| Original language | English |
|---|---|
| Pages (from-to) | 703-710 |
| Number of pages | 8 |
| Journal | Neural Computing and Applications |
| Volume | 22 |
| Issue number | 3-4 |
| DOIs | |
| Publication status | Published - 2013 Jan 1 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Software
- Artificial Intelligence
Cite this
}
Multiclass Lagrangian support vector machine. / Hwang, Jae Pil; Choi, Baehoon; Hong, In Wha; Kim, Euntai.
In: Neural Computing and Applications, Vol. 22, No. 3-4, 01.01.2013, p. 703-710.Research output: Contribution to journal › Article
TY - JOUR
T1 - Multiclass Lagrangian support vector machine
AU - Hwang, Jae Pil
AU - Choi, Baehoon
AU - Hong, In Wha
AU - Kim, Euntai
PY - 2013/1/1
Y1 - 2013/1/1
N2 - A support vector machine (SVM) has been developed for two-class problems, although its application to multiclass problems is not straightforward. This paper proposes a new Lagrangian SVM (LSVM) for application to multiclass problems. The multiclass Lagrangian SVM is formulated as a single optimization problem considering all the classes together, and a training method tailored to the multiclass problem is presented. A multiclass output representation matrix is defined to simplify the optimization formulation and associated training method. The proposed method is applied to some benchmark datasets in repository, and its effectiveness is demonstrated via simulation.
AB - A support vector machine (SVM) has been developed for two-class problems, although its application to multiclass problems is not straightforward. This paper proposes a new Lagrangian SVM (LSVM) for application to multiclass problems. The multiclass Lagrangian SVM is formulated as a single optimization problem considering all the classes together, and a training method tailored to the multiclass problem is presented. A multiclass output representation matrix is defined to simplify the optimization formulation and associated training method. The proposed method is applied to some benchmark datasets in repository, and its effectiveness is demonstrated via simulation.
UR - http://www.scopus.com/inward/record.url?scp=84874022703&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84874022703&partnerID=8YFLogxK
U2 - 10.1007/s00521-011-0755-7
DO - 10.1007/s00521-011-0755-7
M3 - Article
AN - SCOPUS:84874022703
VL - 22
SP - 703
EP - 710
JO - Neural Computing and Applications
JF - Neural Computing and Applications
SN - 0941-0643
IS - 3-4
ER -