Linganisha mbinu

	Random Forest ya Nusu-Msimamizi	Uenezaji wa Lebo	Msitu Nasibu
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning	Machine learning
Mwaka wa asili	2009	2002	2001
Mwanzilishi	Leistner, C., Saffari, A., Santner, J., & Bischof, H.	Zhu, X. & Ghahramani, Z.	Breiman, L.
Aina	Semi-supervised ensemble classifier	Graph-based semi-supervised classification	Ensemble (bagging of decision trees)
Chanzo asilia	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.	Zhu, X., & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Majina mbadala	SSL-RF, semi-supervised forest, label-propagation random forest, self-training random forest	LP, label spreading, graph-based semi-supervised learning, harmonic label propagation	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Zinazohusiana	3	3	4
Muhtasari	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.	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.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.