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In this paper, we propose a new semi-supervised classification algorithm called RDE_self-training, which is an automatic framework for classification of remotely sensed hyperspectral images. The algorithm exploits abundant unlabeled samples when the number of labeled samples is limited to learn an accurate classifier. Train the classifier iteratively on enlarged training set with data editing. Firstly,...
Today's large campus and enterprise networks are characterized by their complexity, i.e. containing thousands of hosts, and diversity, i.e. with various applications and usage patterns. To effectively manage and secure such networks, network operators and system administrators are faced with the challenge of characterizing, profiling and tracking activity patterns passing through their networks. Because...
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