title: Design of a multiple kernel learning algorithm for LS-SVM by convex programming.
abstract: As a kernel based method, the performance of least squares support vector machine (LS-SVM) depends on the selection of the kernel as well as the regularization parameter (Duan, Keerthi, & Poo, 2003). Cross-validation is efficient in selecting a single kernel and the regularization parameter; however, it suffers from heavy computational cost and is not flexible to deal with multiple kernels. In this paper, we address the issue of multiple kernel learning for LS-SVM by formulating it as semidefinite programming (SDP). Furthermore, we show that the regularization parameter can be optimized in a unified framework with the kernel, which leads to an automatic process for model selection. Extensive experimental validations are performed and analyzed.
authors: School of Mathematics and Computational Science, China University of Petroleum, Dongying 257061, China.
authors: Ling
authors: L
authors: Jian
authors: Zhonghang
authors: Z
authors: Xia
authors: Xijun
authors: X
authors: Liang
authors: Chuanhou
authors: C
authors: Gao
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D006801:Humans; D055641:Mathematical Concepts; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D012984:Software / Q000592:standards; D012985:Software Design