Version 2 2024-06-05, 11:49Version 2 2024-06-05, 11:49
Version 1 2016-07-26, 16:58Version 1 2016-07-26, 16:58
conference contribution
posted on 2024-06-05, 11:49authored byT Le, P Duong, M Dinh, TD Nguyen, TV Nguyen, Q Phung
Due to the prevalence of unlabeled data, semi-supervised learning has drawn significant attention and has been found applicable in many real-world applications. In this paper, we present the so-called Budgeted Semi-supervised Sup-port Vector Machine (BS3VM), a method that leverages the excellent generalization capacity of kernel-based method with the adjacent and distributive information carried in a spectral graph for semi-supervised learning purpose. The fact that the optimization problem of BS3VM can be solved directly in the primal form makes it fast and efficient in memory usage. We validate the proposed method on several benchmark datasets to demonstrate its accuracy and efficiency. The experimental results show that BS3VM can scale up efficiently to the large-scale datasets where it yields a comparable classification accuracy while simultaneously achieving a significant computational speed-up compared with the baselines.
History
Pagination
377-386
Location
New York, N.Y.
Start date
2016-06-25
End date
2016-06-29
ISBN-13
9780996643115
Language
eng
Publication classification
E Conference publication, E1 Full written paper - refereed
Copyright notice
2016, AUAI Press
Editor/Contributor(s)
Ihler A, Janzing D
Title of proceedings
UAI 2016: Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence
Event
Uncertainty in Artificial Intelligence. Conference (32nd : 2016 : New York, N.Y.)