An Improved Adaboost Training Algorithm
Hongyin Ni · Journal of Jilin University(Science Edition) · 2011
In view of the problem of degradation issues as well as the distribution of target class weights adapted to the phenomenon that may arise in the training process of the traditional Adaboost algorithm,the authors introduced a few improved methods to these problems.The article presented a modified Adaboost algorithm based on the adjusted weighted error distribution to limit the expansion weights.In addition,the Adaboost algorithm improved the classifier output forms,i.e.,using output of the probability value instead of the discrete value and increased the detection rate more dramatically.Experiment shows that the test rate of the improved Adaboost algorithm could achieve excellent results in the Inria data set.There are good prospects of application in the field of video security surveillance.