User-Based Active Learning

  title={User-Based Active Learning},
  author={Christin Seifert and Michael Granitzer},
  journal={2010 IEEE International Conference on Data Mining Workshops},
Active learning has been proven a reliable strategy to reduce manual efforts in training data labeling. Such strategies incorporate the user as oracle: the classifier selects the most appropriate example and the user provides the label. While this approach is tailored towards the classifier, more intelligent input from the user may be beneficial. For instance, given only one example at a time users are hardly able to determine whether this example is an outlier or not. In this paper we propose… CONTINUE READING
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