TY - GEN
T1 - Geometric approach of quasi-linear kernel composition for support vector machine
AU - Li, Weite
AU - Hu, Jinglu
PY - 2015/9/28
Y1 - 2015/9/28
N2 - 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.
AB - 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.
KW - Kernel
KW - Multiaccess communication
UR - http://www.scopus.com/inward/record.url?scp=84951157596&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84951157596&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2015.7280384
DO - 10.1109/IJCNN.2015.7280384
M3 - Conference contribution
AN - SCOPUS:84951157596
T3 - Proceedings of the International Joint Conference on Neural Networks
BT - 2015 International Joint Conference on Neural Networks, IJCNN 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - International Joint Conference on Neural Networks, IJCNN 2015
Y2 - 12 July 2015 through 17 July 2015
ER -