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Semi-supervised learning by disagreement
Authors:Zhi-Hua Zhou  Ming Li
Affiliation:1. National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210093, China
Abstract: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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