Budgeted semi-supervised support vector machine

Trung Le, Phuong Duong, Mi Dinh, Tu Dinh Nguyen, Vu Nguyen, Dinh Phung

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

14 Citations (Scopus)


Due to the prevalence of unlabeled data, semisupervised learning has drawn significant attention and has been found applicable in many realworld 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.

Original languageEnglish
Title of host publicationUncertainty in Artificial Intelligence
Subtitle of host publicationProceedings of the Thirty-Second Conference (2016)
EditorsAlexander Ihler, Dominik Janzing
Place of PublicationCorvallis OR USA
PublisherAUAI Press
Number of pages10
ISBN (Print)9780996643115
Publication statusPublished - 1 Jan 2016
Externally publishedYes
EventConference in Uncertainty in Artificial Intelligence 2016 - Jersey City, United States of America
Duration: 25 Jun 201629 Jun 2016
Conference number: 32nd
https://dl.acm.org/doi/proceedings/10.5555/3020948 (Proceedings)


ConferenceConference in Uncertainty in Artificial Intelligence 2016
Abbreviated titleUAI 2016
Country/TerritoryUnited States of America
CityJersey City
Internet address

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