Salīdzināt metodes

	Puspašvadāmā gradientu pastiprināšana	Daļēji uzraudzīts Random Forest
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2006–2010s	2009
Autors	Chapelle, Scholkopf & Zien (eds.); applied to GBM variants in subsequent literature	Leistner, C., Saffari, A., Santner, J., & Bischof, H.
Tips	Semi-supervised ensemble (self-training + gradient boosted trees)	Semi-supervised ensemble classifier
Pirmavots	Yarowsky, D. (1995). Unsupervised word sense disambiguation rivaling supervised methods. Proceedings of ACL 1995, 189–196. (Foundational self-training framework underlying pseudo-label approaches.)	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.
Citi nosaukumi	pseudo-label gradient boosting, self-training GBM, semi-supervised GBT, label-propagation boosting	SSL-RF, semi-supervised forest, label-propagation random forest, self-training random forest
Saistītās	6	3
Kopsavilkums	Semi-supervised gradient boosting combines gradient boosted trees with self-training or pseudo-labeling to exploit large pools of unlabeled data alongside a small labeled set. An initial GBM fit on labeled data assigns confident predictions to unlabeled examples; those pseudo-labeled points are folded back into training and the model is re-boosted, iterating until convergence. This allows practitioners to harness cheap unlabeled data when labels are scarce or expensive.	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.