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

Bo Zhou, Di Fu, Chao Dong, Takayuki Furuzuki

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトルProceedings of the International Joint Conference on Neural Networks
出版社Institute of Electrical and Electronics Engineers Inc.
2015-September
ISBN(印刷版)9781479919604, 9781479919604, 9781479919604, 9781479919604
DOI
出版ステータスPublished - 2015 9 28
イベントInternational Joint Conference on Neural Networks, IJCNN 2015 - Killarney, Ireland
継続期間: 2015 7 122015 7 17

Other

OtherInternational Joint Conference on Neural Networks, IJCNN 2015
国/地域Ireland
CityKillarney
Period15/7/1215/7/17

ASJC Scopus subject areas

  • ソフトウェア
  • 人工知能

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