From Batch to Transductive Online Learning
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.
Cite
Text
Kakade and Kalai. "From Batch to Transductive Online Learning." Neural Information Processing Systems, 2005.Markdown
[Kakade and Kalai. "From Batch to Transductive Online Learning." Neural Information Processing Systems, 2005.](https://mlanthology.org/neurips/2005/kakade2005neurips-batch/)BibTeX
@inproceedings{kakade2005neurips-batch,
title = {{From Batch to Transductive Online Learning}},
author = {Kakade, Sham and Kalai, Adam Tauman},
booktitle = {Neural Information Processing Systems},
year = {2005},
pages = {611-618},
url = {https://mlanthology.org/neurips/2005/kakade2005neurips-batch/}
}