Citation:
S. M. Kakade and A. Kalai, From Batch to Transductive Online Learning. Advances in Neural Information Processing Systems 18 (NIPS 2005): , 2006.
Abstract:
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 di- rection. 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.See also: 2006