Sammenlign metoder

	Online Gradient Boosting	Boosting
Fagfelt	Maskinlæring	Maskinlæring
Familie	Machine learning	Machine learning
Opprinnelsesår	2011–2015	1990–1997
Opphavsperson	Grubb, A. & Bagnell, J. A.; Beygelzimer, A. et al.	Schapire, R. E.; Freund, Y.
Type	Online ensemble (sequential boosting on streaming data)	Sequential ensemble (iterative reweighting)
Opprinnelig kilde	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.	Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139.
Alias	OGB, streaming gradient boosting, incremental gradient boosting, online boosting with gradient descent	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble
Relaterte	6	6
Sammendrag	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.	Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy.