We investigate AdaBoost and bipartite version of RankBoost abilities to minimize AUC and its application for score level fusion in multimodal biometric systems. To do this, we customize two methods of weak learner training. Empirical results show comparable AUC for AdaBoost and RankBoost.B which previously was addressed theoretically. We demonstrate exhaustive results among state of the art cla...

Face Detection is an essential first step of the face recognition process, and has significant effects on face feature extraction and the effects of face recognition. Face detection has extensive research value and significance. This paper deeply researches and analysis the principle, merits and demerits of the classic AdaBoost face detection algorithm and ASM algorithm based on point distribut...

Boosting algorithms with l1-regularization are of interest because l1 regularization leads to sparser composite classifiers. Moreover, Rosset et al. have shown that for separable data, standard lpregularized loss minimization results in a margin maximizing classifier in the limit as regularization is relaxed. For the case p = 1, we extend these results by obtaining explicit convergence bounds o...

Pedestrian detection is one of the hot research problems in computer vision field. The Cascade AdaBoost System is a commonly used algorithm in this region. However, when the training datasets become larger, it is still a time consuming process to build one Adaboost classifier. In this paper we detail an implementation of the AdaBoost algorithm using the NVIDIA CUDA framework based on the haar f...

The use of SVM (Support Vector Machine) as component classifier in AdaBoost may seem like going against the grain of the Boosting principle since SVM is not an easy classifier to train. Moreover, Wickramaratna et al. [2001. Performance degradation in boosting. In: Proceedings of the Second International Workshop on Multiple Classifier Systems, pp. 11–21] show that AdaBoost with strong component...

Support Vector Machines (SVMs) and Adaptive Boosting (AdaBoost) are two successful classification methods. They are essentially the same as they both try to maximize the minimal margin on a training set. In this work, we present an even platform to compare these two learning algorithms in terms of their test error, margin distribution and generalization power. Two basic models of polynomials an...

In this note, we discuss the boosting algorithm AdaBoost and identify two of its main drawbacks: it cannot be used in the boosting by ltering framework and it is not noise resistant. In order to solve them, we propose a modiication of the weighting system of AdaBoost. We prove that the new algorithm is in fact a boosting algorithm under the condition that the sequence of advantages generated by...

This paper examines the Asymmetric AdaBoost algorithm introduced by Viola and Jones for cascaded face detection. The Viola and Jones face detector uses cascaded classifiers to successively filter, or reject, non-faces. In this approach most non-faces are easily rejected by the earlier classifiers in the cascade, thus reducing the overall number of computations. This requires earlier cascade cla...