Budgeted semi-supervised support vector machine

Le, Trung, Duong, Phuong, Dinh, Mi, Nguyen, Tu Dinh, Nguyen, Vu and Phung, Dinh 2016, Budgeted semi-supervised support vector machine, in UAI 2016: Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence, AUAI Press, Corvallis, Or., pp. 377-386.

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Title Budgeted semi-supervised support vector machine
Author(s) Le, Trung
Duong, Phuong
Dinh, Mi
Nguyen, Tu Dinh
Nguyen, Vu
Phung, DinhORCID iD for Phung, Dinh orcid.org/0000-0002-9977-8247
Conference name Uncertainty in Artificial Intelligence. Conference (32nd : 2016 : New York, N.Y.)
Conference location New York, N.Y.
Conference dates 25-29 Jun. 2016
Title of proceedings UAI 2016: Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence
Editor(s) Ihler, A.
Janzing, D.
Publication date 2016
Start page 377
End page 386
Total pages 11
Publisher AUAI Press
Place of publication Corvallis, Or.
Summary 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.
ISBN 9780996643115
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2016, AUAI Press
Persistent URL http://hdl.handle.net/10536/DRO/DU:30085089

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