Compară metode

	Semi-supervised Boosting	AdaBoost	Propagarea etichetelor
Domeniu	Învățare automată	Învățare automată	Învățare automată
Familie	Machine learning	Machine learning	Machine learning
Anul apariției	1999–2009	1997	2002
Autorul original	Mallapragada, P. K.; Bennett, K. P.; and others	Freund, Y. & Schapire, R.E.	Zhu, X. & Ghahramani, Z.
Tip	Semi-supervised ensemble method	Ensemble (sequential boosting of weak learners)	Graph-based semi-supervised classification
Sursa seminală	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.	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.	Zhu, X., & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University.
Denumiri alternative	SemiBoost, SSL boosting, boosting with unlabeled data, semi-supervised ensemble boosting	AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma	LP, label spreading, graph-based semi-supervised learning, harmonic label propagation
Înrudite	5	5	3
Rezumat	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.	AdaBoost (Adaptive Boosting) is the original boosting algorithm, introduced by Yoav Freund and Robert Schapire in 1997, that combines a sequence of simple weak learners by giving more weight to the observations they get wrong. The forerunner of gradient boosting, it is simple, interpretable, and a strong baseline for classification.	Label Propagation is a graph-based semi-supervised learning algorithm introduced by Zhu and Ghahramani in 2002 that spreads class labels from a small set of labeled nodes to a large set of unlabeled nodes by iteratively diffusing label information along the edges of a similarity graph, exploiting the manifold structure of the data.