【摘要】 为提高支持向量机的模式分类性能,综合模糊支持向量机和球形支持向量机等方法,提出一种模糊最小包含球(FMEB)支持向量机,对于模式分类问题,通过引入模糊隶属度,寻找2个分别包含二类模式的同心最小包含球,使类间间隔最大化,同时二类模式类内分布最小化,从而增强泛化性和鲁棒性。实验结果证明FMEB的模式分类性能优于其他方法。更多还原

【Abstract】 In order to improve the classification performance of hypersphere Support Vector Machine(SVM),this paper proposes Fuzzy Minimum Enclosing Ball(FMEB) SVM by integrating several state-of-art classification methods such as Fuzzy Support Vector Machine(FSVM) and Hypersphere Support Vector Machine(HSVM).For pattern classification problem,the basic idea of FMEB is to find two optimal minimum enclosing hyperspheres by introducing fuzzy membership,so that each binary class is enclosed by them respectively,and the margin between one class pattern and the enclosing hypersphere is maximized,thus improving the generalization performance and robustness of hypersphere SVM.Experimental results prove that FMEB is more effective than other methods.更多还原