Sciweavers

Share
JMLR
2010

Generalized Expectation Criteria for Semi-Supervised Learning with Weakly Labeled Data

9 years 6 months ago
Generalized Expectation Criteria for Semi-Supervised Learning with Weakly Labeled Data
In this paper, we present an overview of generalized expectation criteria (GE), a simple, robust, scalable method for semi-supervised training using weakly-labeled data. GE fits model parameters by favoring models that match certain expectation constraints, such as marginal label distributions, on the unlabeled data. This paper shows how to apply generalized expectation criteria to two classes of parametric models: maximum entropy models and conditional random fields. Experimental results demonstrate accuracy improvements over supervised training and a number of other stateof-the-art semi-supervised learning methods for these models.
Gideon S. Mann, Andrew McCallum
Added 19 May 2011
Updated 19 May 2011
Type Journal
Year 2010
Where JMLR
Authors Gideon S. Mann, Andrew McCallum
Comments (0)
books