@inproceedings{9ebc77f8c70e4846b2d384a496e5c32d,
title = "Optimization of combined kernel function for SVM based on large margin learning theory",
abstract = "Kernel function plays a very important role in the performance of SVM. In order to improve generalization capability of SVM classifier, this paper proposes a new mechanism to optimize the parameters of combined kernel function by using large margin learning theory and a genetic algorithm, which aims to search the optimal parameters for the combined kernel function. This approach leads SVM to attain the maximum margin in the training dataset. The combined kernel function and the parameters obtained by the proposed approach leads to a better performance and results in a better SVM classifier. Both numerical simulation results and theoretical analysis show the effectiveness and feasibility of the proposed approach.",
keywords = "Combined kernel function, Genetic algorithm, Large margin learning, Optimization, SVM",
author = "Mingzhu LU and Jianbing HUO and CHEN, {C. L. Philip} and Xizhao WANG",
year = "2008",
doi = "10.1109/ICSMC.2008.4811301",
language = "English",
isbn = "9781424423835",
series = "IEEE International Conference on Systems, Man and Cybernetics",
publisher = "IEEE",
pages = "353--358",
booktitle = "Proceedings : 2008 IEEE International Conference on Systems, Man and Cybernetics",
note = "2008 IEEE International Conference on Systems, Man and Cybernetics, SMC 2008 ; Conference date: 12-10-2008 Through 15-10-2008",
}