QQ客服 官方微博 反馈留言

	AdaBoost algorithm using multi-step correction
	Jiang Yan ; Ding Xiaoqing
	2010-10-12 ; 2010-10-12
关键词	Practical Theoretical or Mathematical/ face recognition gradient methods/ AdaBoost algorithm multi-step correction adaptive boosting algorithm/ B6135E Image recognition B0260 Optimisation techniques B0290F Interpolation and function approximation (numerical analysis) B0290H Linear algebra (numerical analysis) C5260B Computer vision and image processing techniques C1180 Optimisation techniques C4130 Interpolation and function approximation (numerical analysis) C4140 Linear algebra (numerical analysis)
中文摘要	The convergence of the traditional AdaBoost (adaptive boosting) algorithm is improved by an AdaBoost algorithm with multi-step correction. In the algorithm, the update of the distribution of the training samples is related not only to the current classifier, but to previous classifiers as well. The algorithm modifies the weights of previously generated classifiers when a new classifier is aggregated. The experiments on the UCI "Diabetes", "Heart statlog", and "Breast cancer Wisconsin" datasets indicate that the modified algorithm achieves better performance in both training and test errors than AdaBoost. The multi-step correction not only enhances the search efficiency for new member classifiers, but further improves the overall performance of the classifier ensemble as well.
语种	中文
出版者	Tsinghua University Press ; China
内容类型	期刊论文
源URL	[http://hdl.handle.net/123456789/82591]
专题	清华大学
推荐引用方式 GB/T 7714	Jiang Yan,Ding Xiaoqing. AdaBoost algorithm using multi-step correction[J],2010, 2010.
APA	Jiang Yan,&Ding Xiaoqing.(2010).AdaBoost algorithm using multi-step correction..
MLA	Jiang Yan,et al."AdaBoost algorithm using multi-step correction".(2010).

个性服务

查看访问统计

相关权益政策

暂无数据

收藏/分享

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。