As a machine learning algorithm, AdaBoost has obtained considerable success in data classification and object detection. Later its generalized version called Real AdaBoost was proposed by Schapire and Singer.Real AdaBoost increasesweights for misclassified samples and decreases weights for correctly classified samples in every iteration. This kind of weight adjustment focuses on the samples with large weights and tries to make them correctly classified in future runs. However, it may lead to the misclassification of some other samples that have been correctly classified in
previous runs. If we can curb this kind of misclassification during the boosting process, a faster training can be achieved. Based on this assumption, we propose Parameterized AdaBoost in which a parameter is devised to penalize the misclassification of samples that have already been correctly classified. Then we analyse that samples with positive classification margins in Parameterized AdaBoost are more than inRealAdaBoost. Experimental results show that our approach achieves a faster convergence of the training error and also improves the generalization error to some degree
when compared with Real AdaBoost.
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