Learning from Labeled and Unlabeled Data Using Graph Mincuts
Abstract
Many application domains suffer from not having enough labeled training data for learning. However, large amounts of unlabeled examples can often be gathered cheaply. As a result, there has been a great deal of work in recent years on how unlabeled data can be used to aid classification. We consider an algorithm based on finding minimum cuts in graphs, that uses pairwise relationships among the examples in order to learn from both labeled and unlabeled data.
Cite
Text
Blum and Chawla. "Learning from Labeled and Unlabeled Data Using Graph Mincuts." International Conference on Machine Learning, 2001. doi:10.1184/R1/6606860.V1Markdown
[Blum and Chawla. "Learning from Labeled and Unlabeled Data Using Graph Mincuts." International Conference on Machine Learning, 2001.](https://mlanthology.org/icml/2001/blum2001icml-learning/) doi:10.1184/R1/6606860.V1BibTeX
@inproceedings{blum2001icml-learning,
title = {{Learning from Labeled and Unlabeled Data Using Graph Mincuts}},
author = {Blum, Avrim and Chawla, Shuchi},
booktitle = {International Conference on Machine Learning},
year = {2001},
pages = {19-26},
doi = {10.1184/R1/6606860.V1},
url = {https://mlanthology.org/icml/2001/blum2001icml-learning/}
}