Budgeted Semi-supervised Support Vector Machine

Trung Le, Phuong Duong, Mi Dinh, Tu Dinh Nguyen, Vu Nguyen, Dinh Phung
Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence, PMLR R14:336-345, 2016.

Abstract

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 Support 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.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR14-le16a, title = {Budgeted Semi-supervised Support Vector Machine}, author = {Le, Trung and Duong, Phuong and Dinh, Mi and Nguyen, Tu Dinh and Nguyen, Vu and Phung, Dinh}, booktitle = {Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence}, pages = {336--345}, year = {2016}, editor = {Ihler, Alexander and Janzing, Dominik}, volume = {R14}, series = {Proceedings of Machine Learning Research}, month = {25--29 Jun}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r14/main/assets/le16a/le16a.pdf}, url = {https://proceedings.mlr.press/r14/le16a.html}, abstract = {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 Support 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.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Budgeted Semi-supervised Support Vector Machine %A Trung Le %A Phuong Duong %A Mi Dinh %A Tu Dinh Nguyen %A Vu Nguyen %A Dinh Phung %B Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2016 %E Alexander Ihler %E Dominik Janzing %F pmlr-vR14-le16a %I PMLR %P 336--345 %U https://proceedings.mlr.press/r14/le16a.html %V R14 %X 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 Support 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. %Z Reissued by PMLR on 04 October 2026.
APA
Le, T., Duong, P., Dinh, M., Nguyen, T.D., Nguyen, V. & Phung, D.. (2016). Budgeted Semi-supervised Support Vector Machine. Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R14:336-345 Available from https://proceedings.mlr.press/r14/le16a.html. Reissued by PMLR on 04 October 2026.

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