Fast Adaboost Training Algorithm by Dynamic Weight Trimming

Abstract

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.