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
Y1 - 2012
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 -