Intimate Learning: A Novel Approach for Combining Labelled and Unlabelled Data

This paper describes a bootstrapping method
closely related to co-training and scoped-learning and is used for Web information extraction task -learning course names from web pages in which we use very few labelled items as seed data (10 web pages) and combine with an unlabelled set (174 web pages). The overall performance improved the precision/recall from 3.11%/0.31% for a baseline EM-based method to 44.7%/44.1% for intimate learning. They used the WebKB dataset.

In co-training there are two views of the same data but one class - but in their approach - there's one view but labeled into two classes (target and intimate classes)