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Month: October 2014

Budget Optimization for Active Learning in Data Streams (Master’s Thesis)

2014/10/222015/10/19 Daniel Kottke

Active learning is a subfield of machine learning that aims to reduce the number of labeled information while receiving the same classification performance. As it is easy to capture unlabeled information from sensors but expensive to annotate this data, the influence of active learning increases fast. Personalized search engines, for instance, need user feedbacks to […]

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