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.
Financial supported by Office of Science and Technology of Guangdong province in China (No: C10909) and by Department of Science and Technology of Guangzhou city in China (No: 2003Z3-D0091)
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© 2005 Springer-Verlag Berlin Heidelberg
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Zhu, Yf., Tian, Lf., Mao, Zy., LI, W. (2005). Mixtures of Kernels for SVM Modeling. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3610. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539087_76
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DOI: https://doi.org/10.1007/11539087_76
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28323-2
Online ISBN: 978-3-540-31853-8
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