Error Bounds for Transductive Learning via Compression and Clustering
Abstract
This paper is concerned with transductive learning. Although transduc- tion appears to be an easier task than induction, there have not been many provably useful algorithms and bounds for transduction. We present ex- plicit error bounds for transduction and derive a general technique for devising bounds within this setting. The technique is applied to derive error bounds for compression schemes such as (transductive) SVMs and for transduction algorithms based on clustering.
Cite
Text
Derbeko et al. "Error Bounds for Transductive Learning via Compression and Clustering." Neural Information Processing Systems, 2003.Markdown
[Derbeko et al. "Error Bounds for Transductive Learning via Compression and Clustering." Neural Information Processing Systems, 2003.](https://mlanthology.org/neurips/2003/derbeko2003neurips-error/)BibTeX
@inproceedings{derbeko2003neurips-error,
title = {{Error Bounds for Transductive Learning via Compression and Clustering}},
author = {Derbeko, Philip and El-Yaniv, Ran and Meir, Ron},
booktitle = {Neural Information Processing Systems},
year = {2003},
pages = {1085-1092},
url = {https://mlanthology.org/neurips/2003/derbeko2003neurips-error/}
}