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Design of a multiple kernel learning algorithm for LS-SVM by convex programming
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DOI：10.1016/j.neunet.2011.03.009.png)
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Abstract
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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. (C) 2011 Elsevier Ltd. All rights reserved.
Keywords:
Least squares support vector machines
Multiple kernel learning
Convex optimization
Semidefinite programming
Quadratically constrained quadratic programming
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