Abstract：The Minimum Class Variance Support Vector Machine (MCVSVM) takes into consideration both the samples in the boundaries and the distribution of the classes. However, only the information in the non-null space of the within-class scatter matrix is utilized in the case of small sample size. To further improve the classification performance, in this paper the Null Space Classifier (NSC) which is rooted in the null space is first presented, then an Ensemble Classifier (EC) is proposed by fusing the MCVSVM and the NSC. Different form the MCVSVM and the NSC, the EC considers the information both in the non-null space and in the null space and has better generalizability. Finally, experimental results on several real datasets indicate the effectiveness of the EC.
王晓明，王士同. 最小类方差支持向量机与零空间分类器的集成[J]. 模式识别与人工智能, 2010, 23(4): 441-449.
WANG Xiao-Ming, WANG Shi-Tong. Ensemble Classifier Based on Minimum Class Variance SVM and Null Space Classifier. , 2010, 23(4): 441-449.
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