Abstract
This paper presents a Transductive Support Vector Machine (TSVM) with quasi-linear kernel based on a clustering assumption for semi-supervised classification. Since the potential separating boundary is located in low density area between classes, a modified density clustering method by considering label information is firstly introduced to extract the information of potential separating boundary in low density region between different classes. Then the information is used to compose a quasi-linear kernel for the TSVM. The optimization of TSVM is further speeded up by developing a pairwise label switching method on minimal sets. Experiment results on benchmark datasets show that the proposed method is effective and improves classification performances.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the International Joint Conference on Neural Networks |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Volume | 2015-September |
| ISBN (Print) | 9781479919604, 9781479919604, 9781479919604, 9781479919604 |
| DOIs | |
| Publication status | Published - 2015 Sep 28 |
| Event | International Joint Conference on Neural Networks, IJCNN 2015 - Killarney, Ireland Duration: 2015 Jul 12 → 2015 Jul 17 |
Other
| Other | International Joint Conference on Neural Networks, IJCNN 2015 |
|---|---|
| Country | Ireland |
| City | Killarney |
| Period | 15/7/12 → 15/7/17 |
Fingerprint
Keywords
- Accuracy
- Kernel
- Support vector machines
- Switches
ASJC Scopus subject areas
- Software
- Artificial Intelligence
Cite this
A Transductive SVM with quasi-linear kernel based on cluster assumption for semi-supervised classification. / Zhou, Bo; Fu, Di; Dong, Chao; Furuzuki, Takayuki.
Proceedings of the International Joint Conference on Neural Networks. Vol. 2015-September Institute of Electrical and Electronics Engineers Inc., 2015. 7280485.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A Transductive SVM with quasi-linear kernel based on cluster assumption for semi-supervised classification
AU - Zhou, Bo
AU - Fu, Di
AU - Dong, Chao
AU - Furuzuki, Takayuki
PY - 2015/9/28
Y1 - 2015/9/28
N2 - This paper presents a Transductive Support Vector Machine (TSVM) with quasi-linear kernel based on a clustering assumption for semi-supervised classification. Since the potential separating boundary is located in low density area between classes, a modified density clustering method by considering label information is firstly introduced to extract the information of potential separating boundary in low density region between different classes. Then the information is used to compose a quasi-linear kernel for the TSVM. The optimization of TSVM is further speeded up by developing a pairwise label switching method on minimal sets. Experiment results on benchmark datasets show that the proposed method is effective and improves classification performances.
AB - This paper presents a Transductive Support Vector Machine (TSVM) with quasi-linear kernel based on a clustering assumption for semi-supervised classification. Since the potential separating boundary is located in low density area between classes, a modified density clustering method by considering label information is firstly introduced to extract the information of potential separating boundary in low density region between different classes. Then the information is used to compose a quasi-linear kernel for the TSVM. The optimization of TSVM is further speeded up by developing a pairwise label switching method on minimal sets. Experiment results on benchmark datasets show that the proposed method is effective and improves classification performances.
KW - Accuracy
KW - Kernel
KW - Support vector machines
KW - Switches
UR - http://www.scopus.com/inward/record.url?scp=84951086404&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84951086404&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2015.7280485
DO - 10.1109/IJCNN.2015.7280485
M3 - Conference contribution
AN - SCOPUS:84951086404
SN - 9781479919604
SN - 9781479919604
SN - 9781479919604
SN - 9781479919604
VL - 2015-September
BT - Proceedings of the International Joint Conference on Neural Networks
PB - Institute of Electrical and Electronics Engineers Inc.
ER -