TY - GEN

T1 - A simple algorithm for semi-supervised learning with improved generalization error bound

AU - Ji, Ming

AU - Yang, Tianbao

AU - Lin, Binbin

AU - Jin, Rong

AU - Han, Jiawei

PY - 2012/10/10

Y1 - 2012/10/10

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84867114245&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84867114245&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84867114245

SN - 9781450312851

T3 - Proceedings of the 29th International Conference on Machine Learning, ICML 2012

SP - 1223

EP - 1230

BT - Proceedings of the 29th International Conference on Machine Learning, ICML 2012

T2 - 29th International Conference on Machine Learning, ICML 2012

Y2 - 26 June 2012 through 1 July 2012

ER -