Giang Hoang Nguyen

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This paper presents a new learning approach for pattern classification applications involving imbalanced data sets. In this approach, a clustering technique is employed to resample the original training set into a smaller set of representative training exemplars, represented by weighted cluster centers and their target outputs. Based on the proposed(More)
This paper presents an efficient training approach for support vector machines that will improve their ability to learn from a large or imbalanced data set. Given an original training set, the proposed approach applies unsupervised learning to extract a smaller set of salient training exemplars, which are represented by weighted cluster centers and the(More)
In this article, we propose a new supervised learning approach for pattern classification applications involving large or imbalanced data sets. In this approach, a clustering technique is employed to reduce the original training set into a smaller set of representative training exemplars, represented by weighted cluster centers and their target outputs.(More)
— Pedestrian detection is a vision task with many practical applications in video surveillance, road safety, autonomous driving and military. However, it is much more difficult compared to the detection of other visual objects, because of the tremendous variations in the inner region as well as the outer shape of the pedestrian pattern. In this paper, we(More)
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