Geometric approach of quasi-linear kernel composition for support vector machine

Weite Li, Jinglu Hu

研究成果: Conference contribution

9 被引用数 (Scopus)

抄録

This paper proposes a geometric way to construct a quasi-linear kernel by which a quasi-linear support vector machine (SVM) is performed. A quasi-linear SVM is a SVM with quasi-linear kernel, in which the nonlinear separation boundary is approximated by using multi-local linear boundaries with interpolation. However, the local linearity extraction for the composition of quasi-linear kernel is still an open problem. In this paper, according to the geometric theory, a method based on piecewise linear classifier is proposed to extract the local linearity in a more precise and efficient way. We firstly construct a function set including multiple linear functions and each of those functions reflects one part of linearity of the whole nonlinear separation boundary. Then the obtained local linearity is added as prior information into the composition of quasi-linear kernel. Experimental results on synthetic data sets and real world data sets show that our proposed method is effective to improve classification performances.

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

出版物シリーズ

名前Proceedings of the International Joint Conference on Neural Networks
2015-September

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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