Semi-supervised learning by disagreement
By Zhi-Hua Zhou, Ming Li. Knowledge and Information Systems, 2010
360 citations recorded by the source corpus.
Subfields: Artificial Intelligence
View Paper: https://www.semanticscholar.org/paper/c1ef36be4ac57fdb25fd1ecb6c65d10e4695bfd0
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In Artificial Intelligence
Citations in the corpus
- Transductive Multilabel Learning via Label Set Propagation (2013, background): “Conventional transductive learning approaches focus on single-labeled classification problems [7], [38], [42].”
- Learning with Augmented Class by Exploiting Unlabeled Data (2014, background): “Previous studies (Chapelle, Sch¨olkopf, and Zien 2006; Zhu and Goldberg 2009; Zhou and Li 2010) have disclosed that unlabeled data implies useful information that can help improve the classification performance particularly when the number of training instances is limited.”
- A brief introduction to weakly supervised learning (2018, background): “Disagreement-based methods [5,32,33] generate multiple learners and let them collaborate to exploit unlabeled data, where the disagreement among the learners is crucial to allow the learning process to continue.”
- A survey on semi-supervised learning (2019, background): “Zhou and Li (2010) provided a survey of semi-supervised learning methods relying on multiple base learners.”
- A survey on instance selection for active learning (2012, background): “Two most successful types are: (1) Semi-supervised learning [87,88], which directly utilizes unlabeled instances by taking into account the geometry of data distributions, such as clusters and manifolds, to propagate label information to neighboring data; and (2) Active learning [89,90], which selec”
- A self-training approach for resolving object coreference on the semantic web (2011, methodology): “Our object coreference resolution algorithm is depicted in Algorithm 1, which follows a traditional self-training framework [28].”