The Label Complexity of Mixed-Initiative Classifier Training

  title={The Label Complexity of Mixed-Initiative Classifier Training},
  author={Jina Suh and Xiaojin Zhu and Saleema Amershi},
Mixed-initiative classifier training, where the human teacher can choose which items to label or to label items chosen by the computer, has enjoyed empirical success but without a rigorous statistical learning theoretical justification. We analyze the label complexity of a simple mixed-initiative training mechanism using teaching dimension and active learning. We show that mixed-initiative training is advantageous compared to either computer-initiated (represented by active learning) or human… CONTINUE READING

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