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

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

7 Citations (Scopus)

Abstract

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.

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

Fingerprint

Support vector machines
Chemical analysis
Interpolation
Classifiers

Keywords

  • Kernel
  • Multiaccess communication

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Cite this

Li, W., & Furuzuki, T. (2015). Geometric approach of quasi-linear kernel composition for support vector machine. In Proceedings of the International Joint Conference on Neural Networks (Vol. 2015-September). [7280384] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2015.7280384

Geometric approach of quasi-linear kernel composition for support vector machine. / Li, Weite; Furuzuki, Takayuki.

Proceedings of the International Joint Conference on Neural Networks. Vol. 2015-September Institute of Electrical and Electronics Engineers Inc., 2015. 7280384.

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

Li, W & Furuzuki, T 2015, Geometric approach of quasi-linear kernel composition for support vector machine. in Proceedings of the International Joint Conference on Neural Networks. vol. 2015-September, 7280384, Institute of Electrical and Electronics Engineers Inc., International Joint Conference on Neural Networks, IJCNN 2015, Killarney, Ireland, 15/7/12. https://doi.org/10.1109/IJCNN.2015.7280384
Li W, Furuzuki T. Geometric approach of quasi-linear kernel composition for support vector machine. In Proceedings of the International Joint Conference on Neural Networks. Vol. 2015-September. Institute of Electrical and Electronics Engineers Inc. 2015. 7280384 https://doi.org/10.1109/IJCNN.2015.7280384
Li, Weite ; Furuzuki, Takayuki. / Geometric approach of quasi-linear kernel composition for support vector machine. Proceedings of the International Joint Conference on Neural Networks. Vol. 2015-September Institute of Electrical and Electronics Engineers Inc., 2015.
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