Parameters selection of SVM for function approximation based on Differential Evolution
- 10.2991/iske.2007.90How to use a DOI?
- machine learning, support vector machines, artificial neural networks, differential evolution, function approximation
Support vector machines (SVM) is a new machine learning method, and it has the ability to approximate nonlinear functions with arbitrary accuracy. Right setting parameters are very crucial to learning results and generalization ability of SVM. In this paper, parameters selection is regarded as a compound optimization problem and a modified differential evolution (MDE) algorithm is applied to search the optimal parameters. The modified differential evolution adopts a time-varying crossover probability strategy, which can improve the global convergence ability and robustness of the algorithm. Various examples are simulated and the experiment results demonstrate that this proposed approach has better approximation performance than other approaches
- © 2007, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - ZHOU Shaowu AU - WU Lianghong AU - YUAN Xiaofang AU - TAN Wen PY - 2007/10 DA - 2007/10 TI - Parameters selection of SVM for function approximation based on Differential Evolution BT - Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2007) PB - Atlantis Press SP - 529 EP - 535 SN - 1951-6851 UR - https://doi.org/10.2991/iske.2007.90 DO - 10.2991/iske.2007.90 ID - Shaowu2007/10 ER -