---
title: "Active Learning with Strong and Weak Views: A Case Study on Wrapper Induction"
authors:
  - "Ion Muslea"
  - "Steven Minton"
  - "Craig A. Knoblock"
doi: ""
url: "https://ijcai.org/proceedings/2003/62"
pdf: "https://ijcai.org/proceedings/2003/0062.pdf"
venue: "Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence"
year: 2003
pages: "415-419"
track: ""
keywords:
---

# Active Learning with Strong and Weak Views: A Case Study on Wrapper Induction

Ion Muslea, Steven Minton, Craig A. Knoblock

*Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence*, pages 415-419.

## Abstract

Multi-view learners reduce the need for labeled data by exploiting disjoint sub-sets of features (views), each of which is sufficient for learning. Such alg.orithms assume that each view is a strong view (I.e., perfect learning is possible in each view). We extend the multi-view framework by introducing a novel algorithm, Aggressive Co-Testing, that exploits both strong and weak views; in a weak view, one can learn a concept that is strictly more general or specific than the target concept. Aggressive Co-Testing uses the weak views both for detecting the most informative examples in the domain and for improving the accuracy of the predictions. In a case study on 33 wrapper induction tasks, our algorithm requires significantly fewer labeled examples than existing state-of-the-art approaches.