Mixtures of Kernels for SVM Modeling

• Published in: Advances in Natural Computation pp. 601 - 607
• Main Authors: Zhu, Yan-fei, Tian, Lian-fang, Mao, Zong-yuan, LI, Wei
• Format: Book Chapter Conference Proceeding
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2005; Springer
• Series: Lecture Notes in Computer Science
• Subjects: Applied sciences; Artificial intelligence; Computer science; control theory; systems; Exact sciences and technology; Polynomial Kernel; Radial Basis Function; Radial Basis Function Kernel; Support Vector Machine; Support Vector Machine Modeling; Radial basis function; Statistical analysis; Non linear model; Vector support machine; Modeling; Linear space; Kernel method
• ISBN: 3540283234; 9783540283232
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/11539087_76

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.