Salīdzināt metodes

	Pastiprināšana	Daļēji uzraudzīta pastiprināšana
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	1990–1997	1999–2009
Autors	Schapire, R. E.; Freund, Y.	Mallapragada, P. K.; Bennett, K. P.; and others
Tips	Sequential ensemble (iterative reweighting)	Semi-supervised ensemble method
Pirmavots	Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139.	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.
Citi nosaukumi	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	SemiBoost, SSL boosting, boosting with unlabeled data, semi-supervised ensemble boosting
Saistītās	6	5
Kopsavilkums	Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy.	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.