Porovnat metody

	Online Gradient Boosting	Polozavedené vylepšování gradientem
Obor	Strojové učení	Strojové učení
Rodina	Machine learning	Machine learning
Rok vzniku	2011–2015	2006–2010s
Tvůrce	Grubb, A. & Bagnell, J. A.; Beygelzimer, A. et al.	Chapelle, Scholkopf & Zien (eds.); applied to GBM variants in subsequent literature
Typ	Online ensemble (sequential boosting on streaming data)	Semi-supervised ensemble (self-training + gradient boosted trees)
Původní zdroj	Grubb, A. & Bagnell, J. A. (2011). Generalized Boosting Algorithms for Convex Optimization. Proceedings of the 28th International Conference on Machine Learning (ICML 2011), 1209–1216.	Yarowsky, D. (1995). Unsupervised word sense disambiguation rivaling supervised methods. Proceedings of ACL 1995, 189–196. (Foundational self-training framework underlying pseudo-label approaches.)
Další názvy	OGB, streaming gradient boosting, incremental gradient boosting, online boosting with gradient descent	pseudo-label gradient boosting, self-training GBM, semi-supervised GBT, label-propagation boosting
Příbuzné	6	6
Shrnutí	Online Gradient Boosting adapts the gradient boosting framework for streaming settings where data arrives one sample at a time rather than as a fixed batch. At each step the model computes a pseudo-residual for the incoming observation and updates a weak learner in place, growing an additive ensemble without storing or revisiting past data. This makes it suitable for real-time prediction and large-scale streaming pipelines where retraining from scratch is infeasible.	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.