Conference proceeding
A Simple Algorithm for Semi-supervised Learning with Improved Generalization Error Bound
Proceedings of the 29th International Conference on Machine Learning
29th ( Edinburgh, Scotland, 2012)
06/27/2012
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.
Details
- Title: Subtitle
- A Simple Algorithm for Semi-supervised Learning with Improved Generalization Error Bound
- Creators
- Ming Ji - UIUCTianbao Yang - Michigan State UniversityBinbin Lin - Zhejiang UniversityRong Jin - Michigan State UniversityJiawei Han - UIUC
- Resource Type
- Conference proceeding
- Publication Details
- Proceedings of the 29th International Conference on Machine Learning
- Conference
- 29th ( Edinburgh, Scotland, 2012)
- Language
- English
- Date published
- 06/27/2012
- Academic Unit
- Computer Science
- Record Identifier
- 9984259403602771
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