Mixtures of kernels for SVM modeling

Authors:
Yan-fei Zhu;Lian-fang Tian;Zong-yuan Mao;Wei LI
Affiliations:
College of Automation Science and Engineering, South China University of Technology, GuangZhou, China;College of Automation Science and Engineering, South China University of Technology, GuangZhou, China;College of Automation Science and Engineering, South China University of Technology, GuangZhou, China;College of Automation Science and Engineering, South China University of Technology, GuangZhou, China
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Year:
2005

Citing 3
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Abstract

Kernels are employed in Support Vector Machines (SVM) to map the nonlinear model into a higher dimensional feature space where the linear learning is adopted. The characteristic of kernels has a great impact on learning and predictive results of SVM. Good characteristic for fitting may not represents good characteristic for generalization. After the research on two kinds of typical kernels—global kernel (polynomial kernel) and local kernel (RBF kernel), a new kind of SVM modeling method based on mixtures of kernels is proposed. Through the implementation in Lithopone calcination process, it demonstrates the good performance of the proposed method compared to single kernel.