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	Fast Adaboost training algorithm by dynamic weight trimming
	Jia Hui-Xing ; Zhang Yu-Jin
	2010-10-12 ; 2010-10-12
关键词	Theoretical or Mathematical/ learning (artificial intelligence) sampling methods/ Adaboost training algorithm dynamic weight trimming large dataset weak classifier performance/ C1230L Learning in AI C1140Z Other topics in statistics
中文摘要	This paper presents a novel fast Adaboost training algorithm by dynamic weight trimming, which increases the training speed greatly when dealing with large datasets. At each iteration, the algorithm discards most of the samples with small weight and keeps only the samples with large weight to train the weak classifier. Then it checks the performance of the weak classifier on all the samples, if the weighted error is above 0.5, it will increase the number of training samples and retrain the weak classifier. During training, only a small portion of the samples are used to train the weak classifier, so the speed is increased greatly.
语种	中文
出版者	Science Press ; China
内容类型	期刊论文
源URL	[http://hdl.handle.net/123456789/82159]
专题	清华大学
推荐引用方式 GB/T 7714	Jia Hui-Xing,Zhang Yu-Jin. Fast Adaboost training algorithm by dynamic weight trimming[J],2010, 2010.
APA	Jia Hui-Xing,&Zhang Yu-Jin.(2010).Fast Adaboost training algorithm by dynamic weight trimming..
MLA	Jia Hui-Xing,et al."Fast Adaboost training algorithm by dynamic weight trimming".(2010).

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