From Batch to Transductive Online Learning

  title={From Batch to Transductive Online Learning},
  author={Sham M. Kakade and Adam Tauman Kalai},
It is well-known that everything that is learnable in the difficult online setting, where an arbitrary sequences of examples must be labeled one at a time, is also learnable in the batch setting, where examples are drawn independently from a distribution. We show a result in the opposite direction. We give an efficient conversion algorithm from batch to online that is transductive: it uses future unlabeled data. This demonstrates the equivalence between what is properly and efficiently… CONTINUE READING
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