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Boosting is an approach to machine learning based on the idea of creating a highly accurate prediction rule by combining many… Expand The use of SVM (Support Vector Machine) as component classifier in AdaBoost may seem like going against the grain of the Boosting… Expand We present an algorithm that predicts musical genre and artist from an audio waveform. Our method uses the ensemble learner… Expand Our goal is to automatically segment and recognize basic human actions, such as stand, walk and wave hands, from a sequence of… Expand Boosting is a technique of combining a set weak classifiers to form one high-performance prediction rule. Boosting was… Expand Recently ensemble methods like ADABOOST have been applied successfully in many problems, while seemingly defying the problems of… Expand We give a unified account of boosting and logistic regression in which each learning problem is cast in terms of optimization of… Expand In this paper, we propose a rotation invariant multi-view face detection method based on Real Adaboost algorithm. Human faces are… Expand This paper develops a new approach for extremely fast detection in domains where the distribution of positive and negative… Expand We propse a new boosting algorithm that mends some of the problems that have been detected in the so far most successful boosting… Expand