2026-03-09T00:15:22Z https://hiroshima.repo.nii.ac.jp/oai

oai:hiroshima.repo.nii.ac.jp:02000822 2025-02-18T02:51:40Z 1730444917621

Support Vector Selection for Regression Machines Lee, Wan-Jui Yang, Chih-Cheng Lee, Shie-Jue Orthogonal least-squares over-fitting, gradient descent learning rules error reduction ratio mean square error 500 In this paper, we propose a method to select support vectors to improve the performance of support vector regression machines. First, the orthogonal leastsquares method is adopted to evaluate the support vectors based on their error reduction ratios. By selecting the representative support vectors, we can obtain a simpler model which helps avoid the over-fitting problem. Second, the simplified model is further refined by applying the gradient descent method to tune the parameters of the kernel functions. Learning rules for minimizing the regularized risk functional are derived. Experimental results have shown that our approach can improve effectively the generalization capability of support vector regressors. http://purl.org/coar/resource_type/c_5794 IEEE SMC Hiroshima Chapter 2009-11 VoR 1883-3977 18 5th International Workshop on Computational Intelligence & Applications Proceedings : IWCIA 2009 23 18 5th International Workshop on Computational Intelligence & Applications Proceedings : IWCIA 2009 https://hiroshima.repo.nii.ac.jp/records/2000822 eng http://www.hil.hiroshima-u.ac.jp/iwcia/2009/ open access (c) Copyright by IEEE SMC Hiroshima Chapter.