Сравнение на методи

	AdaBoost	Label Propagation
Област	Машинно обучение	Машинно обучение
Семейство	Machine learning	Machine learning
Година на възникване	1997	2002
Създател	Freund, Y. & Schapire, R.E.	Zhu, X. & Ghahramani, Z.
Тип	Ensemble (sequential boosting of weak learners)	Graph-based semi-supervised classification
Основополагащ източник	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.
Други названия	AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma	LP, label spreading, graph-based semi-supervised learning, harmonic label propagation
Свързани	5	3
Резюме	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.