A Revised AdaBoost Algorithm: FM-AdaBoost

摘要：

In view of ensemble equivalence, this paper proposes a revised AdaBoost algorithm: FM-AdaBoost It can ensure the ensemble error rates are the least by F-module, which filter classifiers after all of the iteration finish. At the same time, with the optional M-module it can ensure the training error rates decreases monotonously, which improves the training velocity effectively. In the end, simulation results show the algorithm is valid.

关键词： adaBoost classifier ensemble of classifiers

作者: Yanfeng Zhang Peikun He

作者单位: School of Information and Electronic Beijing institute of Technology Beijing, China

会议类型: 国际会议

会议名称: The 2010 International Conference on Computer Application and System Modeling(2010计算机应用与系统建模国际会议 ICCASM 2010)

会议地点: 太原

会议语种:英文

页码: 277-281

在线出版日期: 2010-10-22（万方平台首次上网日期，不代表论文的发表时间）