Support vector machine with SOM-based quasi-linear kernel for nonlinear classification

Yuling Lin; Yong Fu; Jinglu Hu

doi:10.1109/IJCNN.2014.6889654

Support vector machine with SOM-based quasi-linear kernel for nonlinear classification

Yuling Lin^*, Yong Fu, Jinglu Hu

^*Corresponding author for this work

Graduate School of Information, Production and Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This paper proposes a self-organizing maps (SOM) based kernel composition method for the quasi-linear support vector machine (SVM). The quasi-linear SVM is SVM model with quasi-linear kernel, in which the nonlinear separation hyperplane is approximated by multiple local linear models with interpolation. The basic idea underlying the proposed method is to use clustering and projection properties of SOM to partition the input space and construct a SOM based quasi-linear kernel. By effectively extracting the distribution information using SOM, the quasi-linear SVM with the SOM-based quasi-linear kernel is expected to have better performance in the cases of high-noise and high-dimension. Experiment results on synthetic datasets and real world datasets show the effectiveness of the proposed method.

Original language	English
Title of host publication	Proceedings of the International Joint Conference on Neural Networks
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3783-3789
Number of pages	7
ISBN (Electronic)	9781479914845
DOIs	https://doi.org/10.1109/IJCNN.2014.6889654
Publication status	Published - 2014 Sept 3
Event	2014 International Joint Conference on Neural Networks, IJCNN 2014 - Beijing, China Duration: 2014 Jul 6 → 2014 Jul 11

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks

Conference

Conference	2014 International Joint Conference on Neural Networks, IJCNN 2014
Country/Territory	China
City	Beijing
Period	14/7/6 → 14/7/11

ASJC Scopus subject areas

Software
Artificial Intelligence

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10.1109/IJCNN.2014.6889654

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Lin, Y., Fu, Y., & Hu, J. (2014). Support vector machine with SOM-based quasi-linear kernel for nonlinear classification. In Proceedings of the International Joint Conference on Neural Networks (pp. 3783-3789). Article 6889654 (Proceedings of the International Joint Conference on Neural Networks). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2014.6889654

Support vector machine with SOM-based quasi-linear kernel for nonlinear classification. / Lin, Yuling; Fu, Yong; Hu, Jinglu.
Proceedings of the International Joint Conference on Neural Networks. Institute of Electrical and Electronics Engineers Inc., 2014. p. 3783-3789 6889654 (Proceedings of the International Joint Conference on Neural Networks).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Lin, Y, Fu, Y & Hu, J 2014, Support vector machine with SOM-based quasi-linear kernel for nonlinear classification. in Proceedings of the International Joint Conference on Neural Networks., 6889654, Proceedings of the International Joint Conference on Neural Networks, Institute of Electrical and Electronics Engineers Inc., pp. 3783-3789, 2014 International Joint Conference on Neural Networks, IJCNN 2014, Beijing, China, 14/7/6. https://doi.org/10.1109/IJCNN.2014.6889654

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title = "Support vector machine with SOM-based quasi-linear kernel for nonlinear classification",

abstract = "This paper proposes a self-organizing maps (SOM) based kernel composition method for the quasi-linear support vector machine (SVM). The quasi-linear SVM is SVM model with quasi-linear kernel, in which the nonlinear separation hyperplane is approximated by multiple local linear models with interpolation. The basic idea underlying the proposed method is to use clustering and projection properties of SOM to partition the input space and construct a SOM based quasi-linear kernel. By effectively extracting the distribution information using SOM, the quasi-linear SVM with the SOM-based quasi-linear kernel is expected to have better performance in the cases of high-noise and high-dimension. Experiment results on synthetic datasets and real world datasets show the effectiveness of the proposed method.",

author = "Yuling Lin and Yong Fu and Jinglu Hu",

year = "2014",

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doi = "10.1109/IJCNN.2014.6889654",

language = "English",

series = "Proceedings of the International Joint Conference on Neural Networks",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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AU - Fu, Yong

AU - Hu, Jinglu

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N2 - This paper proposes a self-organizing maps (SOM) based kernel composition method for the quasi-linear support vector machine (SVM). The quasi-linear SVM is SVM model with quasi-linear kernel, in which the nonlinear separation hyperplane is approximated by multiple local linear models with interpolation. The basic idea underlying the proposed method is to use clustering and projection properties of SOM to partition the input space and construct a SOM based quasi-linear kernel. By effectively extracting the distribution information using SOM, the quasi-linear SVM with the SOM-based quasi-linear kernel is expected to have better performance in the cases of high-noise and high-dimension. Experiment results on synthetic datasets and real world datasets show the effectiveness of the proposed method.

AB - This paper proposes a self-organizing maps (SOM) based kernel composition method for the quasi-linear support vector machine (SVM). The quasi-linear SVM is SVM model with quasi-linear kernel, in which the nonlinear separation hyperplane is approximated by multiple local linear models with interpolation. The basic idea underlying the proposed method is to use clustering and projection properties of SOM to partition the input space and construct a SOM based quasi-linear kernel. By effectively extracting the distribution information using SOM, the quasi-linear SVM with the SOM-based quasi-linear kernel is expected to have better performance in the cases of high-noise and high-dimension. Experiment results on synthetic datasets and real world datasets show the effectiveness of the proposed method.

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Support vector machine with SOM-based quasi-linear kernel for nonlinear classification

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