SemiBoost: Boosting for Semi-Supervised Learning

By Pavan Kumar Mallapragada, Rong Jin, Anil K. Jain, Yi Liu. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009

360 citations recorded by the source corpus.

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A survey on semi-supervised learning (2019, methodology): “The semi-supervised boosting algorithm SemiBoost addresses the problem of selecting data points to be used by the base learners by relying on the manifold assumption, utilizing principles from graph-based methods (Mallapragada et al. 2009).”
Semi-supervised On-Line Boosting for Robust Tracking (2008, methodology): “We use the recently proposed SemiBoost approach by Mallapragada et al. [17] which combines ideas from graph theory and clustering and outperforms other approaches on common machine learning datasets.”
Adaptive Hypergraph Learning and its Application in Image Classification (2012, neutral): “Transductive learning [4], [9], [12], [15]–[17] is a popular approach to address this problem.”
Self-labeled techniques for semi-supervised learning: taxonomy, software and empirical study (2015, background): “In addition, there are recent studies that address multiple assumptions in one model [7,12].”