A Transductive SVM with quasi-linear kernel based on cluster assumption for semi-supervised classification

Bo Zhou, Di Fu, Chao Dong, Takayuki Furuzuki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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 languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks
PublisherInstitute of Electrical and Electronics Engineers Inc.
Volume2015-September
ISBN (Print)9781479919604, 9781479919604, 9781479919604, 9781479919604
DOIs
Publication statusPublished - 2015 Sep 28
EventInternational Joint Conference on Neural Networks, IJCNN 2015 - Killarney, Ireland
Duration: 2015 Jul 122015 Jul 17

Other

OtherInternational Joint Conference on Neural Networks, IJCNN 2015
CountryIreland
CityKillarney
Period15/7/1215/7/17

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Keywords

  • Accuracy
  • Kernel
  • Support vector machines
  • Switches

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Cite this

Zhou, B., Fu, D., Dong, C., & Furuzuki, T. (2015). A Transductive SVM with quasi-linear kernel based on cluster assumption for semi-supervised classification. In Proceedings of the International Joint Conference on Neural Networks (Vol. 2015-September). [7280485] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2015.7280485