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Boosting-based methods have recently led to the state-of-the-art face detection systems. In these systems, weak classifiers to be boosted are based on simple, local, Haar-like features. However, it can be empirically observed that in later stages of the bo ...
The problem we address in this paper is, whether the feature extraction module trained on large amounts of task independent data, can improve the performance of stochastic models? We show that when there is only a small amount of task specific training dat ...
Ensemble algorithms can improve the performance of a given learning algorithm through the combination of multiple base classifiers into an ensemble. In this paper, the idea of using an adaptive policy for training and combining the base classifiers is put ...
In a society which produces and consumes an ever increasing amount of information, methods which can make sense out of all this data become of crucial importance. Machine learning tries to develop models which can make the information load accessible. Thre ...
In a society which produces and consumes an ever increasing amount of information, methods which can make sense out of al1 this data become of crucial importance. Machine learning tries to develop models which can make the information load accessible. Thre ...
Boosting-based methods have recently led to the state-of-the-art face detection systems. In these systems, weak classifiers to be boosted are based on simple, local, Haar-like features. However, it can be empirically observed that in later stages of the bo ...
Ensemble algorithms can improve the performance of a given learning algorithm through the combination of multiple base classifiers into an ensemble. In this paper, the idea of using an adaptive policy for training and combining the base classifiers is put ...
The problem we address in this paper is, whether the feature extraction module trained on large amounts of task independent data, can improve the performance of stochastic models? We show that when there is only a small amount of task specific training dat ...
Boosting is a general method for training an ensemble of classifiers with a view to improving performance relative to that of a single classifier. While the original AdaBoost algorithm has been defined for classification tasks, the current work examines it ...
Ensemble algorithms are general methods for improving the performance of a given learning algorithm. This is achieved by the combination of multiple base classifiers into an ensemble. In this paper, the idea of using an adaptive policy for training and com ...
