Learning with Local and Global Consistency
Abstract
We consider the general problem of learning from labeled and unlabeled data, which is often called semi-supervised learning or transductive in- ference. A principled approach to semi-supervised learning is to design a classifying function which is suf(cid:2)ciently smooth with respect to the intrinsic structure collectively revealed by known labeled and unlabeled points. We present a simple algorithm to obtain such a smooth solution. Our method yields encouraging experimental results on a number of clas- si(cid:2)cation problems and demonstrates effective use of unlabeled data.
Cite
Text
Zhou et al. "Learning with Local and Global Consistency." Neural Information Processing Systems, 2003.Markdown
[Zhou et al. "Learning with Local and Global Consistency." Neural Information Processing Systems, 2003.](https://mlanthology.org/neurips/2003/zhou2003neurips-learning/)BibTeX
@inproceedings{zhou2003neurips-learning,
title = {{Learning with Local and Global Consistency}},
author = {Zhou, Dengyong and Bousquet, Olivier and Lal, Thomas N. and Weston, Jason and Schölkopf, Bernhard},
booktitle = {Neural Information Processing Systems},
year = {2003},
pages = {321-328},
url = {https://mlanthology.org/neurips/2003/zhou2003neurips-learning/}
}