Improved Algorithm for Adaboost with SVM Base Classifiers

摘要：

The relation between the performance of AdaBoost and the performance of base classifiers was analyzed, and the approach of improving the classification performance of AdaBoostSVM was studied. There is inconsistency existed between the accuracy and diversity of base classifiers, and the inconsistency affect generalization performance of the algorithm. A new variable σ-AdaBoostSVM was proposed by adjusting the kernel junction parameter of the base classifier based on the distribution of training samples. The proposed algorithm improves the classification performance by making a balance between the accuracy and diversify of base classifiers. Experimental results indicate the effectiveness of the proposed algorithm.

关键词： Support Vector Machine AdaBoost.

作者: Xiaodan WANG Chongming WU Chunying ZHENG Wei WANG

作者单位: Department of Computer Engineering, Air Force Engineering University

会议类型: 国际会议

会议名称: Firth IEEE International Conference on Cognitive Informatics(第五届认知信息国际会议)

会议地点: 北京

会议语种:英文

页码: 948-952

在线出版日期: 2006-07-17（万方平台首次上网日期，不代表论文的发表时间）