Compară metode

	Random Forest semi-supervizat	Gradient Boosting	Propagarea etichetelor
Domeniu	Învățare automată	Învățare automată	Învățare automată
Familie	Machine learning	Machine learning	Machine learning
Anul apariției	2009	2001	2002
Autorul original	Leistner, C., Saffari, A., Santner, J., & Bischof, H.	Friedman, J. H.	Zhu, X. & Ghahramani, Z.
Tip	Semi-supervised ensemble classifier	Ensemble (sequential boosting of decision trees)	Graph-based semi-supervised classification
Sursa seminală	Leistner, C., Saffari, A., Santner, J., & Bischof, H. (2009). Semi-supervised random forests. In Proceedings of the IEEE 12th International Conference on Computer Vision (ICCV), pp. 506–513. IEEE.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.	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	SSL-RF, semi-supervised forest, label-propagation random forest, self-training random forest	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	LP, label spreading, graph-based semi-supervised learning, harmonic label propagation
Înrudite	3	5	3
Rezumat	Semi-supervised Random Forest (SSL-RF) extends the classic Random Forest by exploiting both labeled and unlabeled training examples. When labeling data is expensive or time-consuming, SSL-RF assigns tentative pseudo-labels to unlabeled observations through the forest itself, then retrains on the enriched dataset, progressively improving accuracy without requiring additional human annotation.	Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost.	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.