Design of a multiple kernel learning algorithm for LS-SVM by convex programming

✍ Scribed by Ling Jian; Zhonghang Xia; Xijun Liang; Chuanhou Gao

• Book ID: 103851496
• Publisher: Elsevier Science
• Year: 2011
• Tongue: English
• Weight: 323 KB
• Volume: 24
• Category: Article
• ISSN: 0893-6080
• DOI: 10.1016/j.neunet.2011.03.009

✦ Synopsis

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). Crossvalidation 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.