So sánh phương pháp

	Boosting	Semi-supervised Boosting
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	1990–1997	1999–2009
Người khởi xướng	Schapire, R. E.; Freund, Y.	Mallapragada, P. K.; Bennett, K. P.; and others
Loại	Sequential ensemble (iterative reweighting)	Semi-supervised ensemble method
Công trình gốc	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.
Tên gọi khác	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	SemiBoost, SSL boosting, boosting with unlabeled data, semi-supervised ensemble boosting
Liên quan	6	5
Tóm tắt	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.