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Many real world classification problems lack of a large number of labeled data for learning an effective classifier. Active learning methods seek to address this problem by reducing the number of labeled instances needed to build an effective classifier. Most current active learning methods, however, are myopic, i.e. select one single unlabelled sample to label at a time. Obviously, such a strategy...
Active learning methods seek to reduce the number of labeled instances needed to train an effective classifier. Most current methods are myopic, i.e. select a single unlabelled sample to label at a time. The batch-mode active learning methods, on the other hand, typically select top N unlabeled samples with maximum score. Such selected samples often cannot guarantee the learner's performance. In this...
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