A Simple Algorithm for Semi-Supervised Learning with Improved Generalization Error Bound
Abstract
In this work, we develop a simple algorithm for semi-supervised regression. The key idea is to use the top eigenfunctions of integral operator derived from both labeled and unlabeled examples as the basis functions and learn the prediction function by a simple linear regression. We show that under appropriate assumptions about the integral operator, this approach is able to achieve an improved regression error bound better than existing bounds of supervised learning. We also verify the effectiveness of the proposed algorithm by an empirical study.
Cite
Text
Ji et al. "A Simple Algorithm for Semi-Supervised Learning with Improved Generalization Error Bound." International Conference on Machine Learning, 2012.Markdown
[Ji et al. "A Simple Algorithm for Semi-Supervised Learning with Improved Generalization Error Bound." International Conference on Machine Learning, 2012.](https://mlanthology.org/icml/2012/ji2012icml-simple/)BibTeX
@inproceedings{ji2012icml-simple,
title = {{A Simple Algorithm for Semi-Supervised Learning with Improved Generalization Error Bound}},
author = {Ji, Ming and Yang, Tianbao and Lin, Binbin and Jin, Rong and Han, Jiawei},
booktitle = {International Conference on Machine Learning},
year = {2012},
url = {https://mlanthology.org/icml/2012/ji2012icml-simple/}
}