Interval Insensitive Loss for Ordinal Classification

  title={Interval Insensitive Loss for Ordinal Classification},
  author={Kostiantyn Antoniuk and Vojtech Franc and V{\'a}clav Hlav{\'a}c},
We address a problem of learning ordinal classifier from partially annotated examples. We introduce an interval-insensitive loss function to measure discrepancy between predictions of an ordinal classifier and a partial annotation provided in the form of intervals of admissible labels. The proposed interval-insensitive loss is an instance of loss functions previously used for learning of different classification models from partially annotated examples. We propose several convex surrogates of… CONTINUE READING
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