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 |
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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 |
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Country/Territory | Ireland |
City | Killarney |
Period | 15/7/12 → 15/7/17 |
Keywords
- Accuracy
- Kernel
- Support vector machines
- Switches
ASJC Scopus subject areas
- Software
- Artificial Intelligence