Journal
KNOWLEDGE AND INFORMATION SYSTEMS
Volume 24, Issue 3, Pages 415-439Publisher
SPRINGER LONDON LTD
DOI: 10.1007/s10115-009-0209-z
Keywords
Machine learning; Data mining; Semi-supervised learning; Disagreement-based semi-supervised learning
Funding
- National Science Foundation of China [60635030, 60721002]
- Jiangsu Science Foundation [BK2008018]
- Jiangsu 333 High-Level Talent Cultivation Program
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In many real-world tasks, there are abundant unlabeled examples but the number of labeled training examples is limited, because labeling the examples requires human efforts and expertise. So, semi-supervised learning which tries to exploit unlabeled examples to improve learning performance has become a hot topic. Disagreement-based semi-supervised learning is an interesting paradigm, where multiple learners are trained for the task and the disagreements among the learners are exploited during the semi-supervised learning process. This survey article provides an introduction to research advances in this paradigm.
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