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

Ming Ji, Tianbao Yang, Binbin Lin, Rong Jin, Jiawei Han

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationProceedings of the 29th International Conference on Machine Learning, ICML 2012
Pages1223-1230
Number of pages8
StatePublished - Oct 10 2012
Event29th International Conference on Machine Learning, ICML 2012 - Edinburgh, United Kingdom
Duration: Jun 26 2012Jul 1 2012

Publication series

NameProceedings of the 29th International Conference on Machine Learning, ICML 2012
Volume2

Other

Other29th International Conference on Machine Learning, ICML 2012
CountryUnited Kingdom
CityEdinburgh
Period6/26/127/1/12

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Education

Fingerprint Dive into the research topics of 'A simple algorithm for semi-supervised learning with improved generalization error bound'. Together they form a unique fingerprint.

Cite this