Link-based Classification using Labeled and Unlabeled Data
Qing Ling Lu, Lise Getoor · 2003
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.