Date of this Version
Advances in Neural Information Processing Systems
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 learnable in a batch model and a transductive online model.
Kakade, S., & Kalai, A. T. (2005). From Batch to Transductive Online Learning. Advances in Neural Information Processing Systems, 18 Retrieved from https://repository.upenn.edu/statistics_papers/470
Date Posted: 27 November 2017