Abstract: Asymmetry is inherent in tasks of object detection where rare positive targets need to be distinguished from enormous negative patterns. That is, to achieve a higher detection rate, the cost of missing a target should be higher than that of a false positive. Cost-sensitive learning is a suitable way for solving such problems. However, most cost-sensitive extensions of AdaBoost are realized by heuristically modifying the weights and confidence parameters of the discrete AdaBoost. It remains unclear whether there is a unified framework to interpret these methods as AdaBoost, clarify their relationships, and further derive the superior real-valued cost-sensitive boosting algorithms. In this paper, according to the three different upper bounds of the asymmetric training error, we not only give a detailed discussion about the various discrete asymmetric AdaBoost algorithms and their relationships, but also derive the real-valued asymmetric boosting algorithms in the form of additive logistic regression with analytical solutions, which are denoted by Asym-Real AdaBoost and Asym-Gentle AdaBoost. Experiments on both face detection and pedestrian detection demonstrate that the proposed approaches are efficient and achieve better performance than the previous AdaBoost methods and discrete asymmetric extensions.