Linganisha mbinu

	CatBoost ya Nusu-Usimamizi	Semi-supervised Gradient Boosting
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	2018 (CatBoost); semi-supervised learning framework predates 2006	2006–2010s
Mwanzilishi	Prokhorenkova et al. (CatBoost); semi-supervised paradigm from Chapelle et al.	Chapelle, Scholkopf & Zien (eds.); applied to GBM variants in subsequent literature
Aina	Semi-supervised ensemble (gradient boosting)	Semi-supervised ensemble (self-training + gradient boosted trees)
Chanzo asilia	Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (2018). CatBoost: unbiased boosting with categorical features. In Advances in Neural Information Processing Systems (NeurIPS), 31.	Yarowsky, D. (1995). Unsupervised word sense disambiguation rivaling supervised methods. Proceedings of ACL 1995, 189–196. (Foundational self-training framework underlying pseudo-label approaches.)
Majina mbadala	SSL CatBoost, semi-supervised gradient boosting with CatBoost, CatBoost with unlabeled data, pseudo-label CatBoost	pseudo-label gradient boosting, self-training GBM, semi-supervised GBT, label-propagation boosting
Zinazohusiana	5	6
Muhtasari	Semi-supervised CatBoost applies CatBoost's ordered gradient boosting framework to settings where only a fraction of training instances carry labels, leveraging unlabeled data through pseudo-labeling or consistency-based strategies to improve model accuracy beyond what labeled data alone would allow.	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.