A Simple Algorithm for Semi-supervised Learning with Improved Generalization Error Bound
Machine Learning
2012-07-03 v1 Machine Learning
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
@article{arxiv.1206.6412,
title = {A Simple Algorithm for Semi-supervised Learning with Improved Generalization Error Bound},
author = {Ming Ji and Tianbao Yang and Binbin Lin and Rong Jin and Jiawei Han},
journal= {arXiv preprint arXiv:1206.6412},
year = {2012}
}
Comments
Appears in Proceedings of the 29th International Conference on Machine Learning (ICML 2012)