Porovnat metody

	Samoučící se posilování	Polosupervizované posilování (Semi-supervised Boosting)
Obor	Strojové učení	Strojové učení
Rodina	Machine learning	Machine learning
Rok vzniku	2010s–2020s	1999–2009
Tvůrce	Various researchers (2010s–2020s)	Mallapragada, P. K.; Bennett, K. P.; and others
Typ	Ensemble (self-supervised + boosting)	Semi-supervised ensemble method
Původní zdroj	Yarowsky, D. (1995). Unsupervised word sense disambiguation rivaling supervised methods. In Proceedings of the 33rd Annual Meeting of the Association for Computational Linguistics (pp. 189–196). ACL.	Mallapragada, P. K., Jin, R., Jain, A. K., & Liu, Y. (2009). SemiBoost: Boosting for Semi-supervised Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(11), 2000–2014.
Další názvy	SSL boosting, self-supervised ensemble boosting, pretext-task boosting, SSL-Boost	SemiBoost, SSL boosting, boosting with unlabeled data, semi-supervised ensemble boosting
Příbuzné	6	5
Shrnutí	Self-supervised boosting integrates self-supervised pretext tasks into the boosting framework — covering AdaBoost, gradient boosting, and their modern variants — to leverage large pools of unlabeled data. By first learning feature representations from unlabeled samples and then running sequential weak-learner ensembles on pseudo-labeled data, it achieves competitive accuracy even when ground-truth labels are scarce.	Semi-supervised Boosting is an ensemble learning paradigm that extends classical boosting algorithms — such as AdaBoost — to exploit both labeled and unlabeled data. By propagating label information through a similarity structure over unlabeled instances, it trains stronger classifiers than supervised boosting alone when labeled data are scarce.