Semi-supervised learning by disagreement

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. Disagreementbased semi-supervised learning is an interesting paradigm, where multiple learners are trained for the task and the disagreements among the learners are exploited during

• Type: Journal
• Year: 2010
• Where: KAIS
• Authors: Zhi-Hua Zhou, Ming Li
• Keywords: Abundant Unlabeled Examples, Disagreementbased Semi-supervised Learning, KAIS 2010, Semi-supervised Learning