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An Optimization Based Framework for Dynamic Batch Mode Active Learning

An Optimization Based Framework for Dynamic Batch Mode Active Learning

This video was recorded at NIPS Workshops, Whistler 2010. Active learning techniques have gained popularity in reducing human effort to annotate data instances for inducing a classifier. When faced with large quantities of unlabeled data, such algorithms automatically select the salient and representative samples for manual annotation. Batch mode active learning schemes have been recently proposed to select a batch of data instances simultaneously, rather than updating the classifier after every single query. While numerical optimization strategies seem a natural choice to address this problem (by selecting a batch of points to ensure that a given objective criterion is optimized), many of the proposed approaches are based on greedy heuristics. Also, all the existing work on batch mode... Show More
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