A Simple Algorithm for Semi-supervised Learning with Improved   Generalization Error Bound
  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. 
Ming Ji (UIUC), Tianbao Yang (Michigan State University), Binbin Lin   (Zhejiang University), Rong Jin (Michigan State Unive...
cs.LG stat.ML