An Auto-Adjustable Semi-Supervised Self-Training Algorithm
AbstractSemi-supervised learning algorithms have become a topic of significant research as an alternative to traditional classification methods which exhibit remarkable performance over labeled data but lack the ability to be applied on large amounts of unlabeled data. In this work, we propose a new semi-supervised learning algorithm that dynamically selects the most promising learner for a classification problem from a pool of classifiers based on a self-training philosophy. Our experimental results illustrate that the proposed algorithm outperforms its component semi-supervised learning algorithms in terms of accuracy, leading to more efficient, stable and robust predictive models. View Full-Text
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Livieris, I.E.; Kanavos, A.; Tampakas, V.; Pintelas, P. An Auto-Adjustable Semi-Supervised Self-Training Algorithm. Algorithms 2018, 11, 139.
Livieris IE, Kanavos A, Tampakas V, Pintelas P. An Auto-Adjustable Semi-Supervised Self-Training Algorithm. Algorithms. 2018; 11(9):139.Chicago/Turabian Style
Livieris, Ioannis E.; Kanavos, Andreas; Tampakas, Vassilis; Pintelas, Panagiotis. 2018. "An Auto-Adjustable Semi-Supervised Self-Training Algorithm." Algorithms 11, no. 9: 139.
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