LINQS

STATISTICAL RELATIONAL LEARNING GROUP @ UMD

Link-based Classification Using Labeled and Unlabeled Data

Qing Lu, Lise Getoor

ICML Workshop on "The Continuum from Labeled to Unlabeled Data in Machine Learning and Data Mining - 2003

Download the publication :

There has been a surge of interest in learning using a mix of labeled and unlabeled data. General approaches include semi-supervised learning and tranductive inference. In this paper we look at some of the unique ways in which unlabeled data can improve performance when doing link-based classification, the classification of objects making use of both object descriptions and the links between objects.

BibTex references

@InProceedings{lu:icmlws03,
  author       = "Lu, Qing and Getoor, Lise",
  title        = "Link-based Classification Using Labeled and Unlabeled Data",
  booktitle    = "ICML Workshop on "The Continuum from Labeled to Unlabeled Data in Machine Learning and Data Mining",
  year         = "2003",
}

Other publications in the database

» Qing Lu
» Lise Getoor