विधियों की तुलना करें

	ऑनलाइन बूस्टिंग	बूस्टिंग	ऑनलाइन बैगिंग
क्षेत्र	मशीन अधिगम	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning	Machine learning
उद्भव वर्ष	2001	1990–1997	2001
प्रवर्तक	Oza, N. C. & Russell, S.	Schapire, R. E.; Freund, Y.	Oza, N. C. & Russell, S.
प्रकार	Online ensemble (incremental boosting)	Sequential ensemble (iterative reweighting)	Online ensemble (streaming bagging)
मौलिक स्रोत	Oza, N. C., & Russell, S. (2001). Online Bagging and Boosting. In Artificial Intelligence and Statistics 2001 (pp. 105–112). Morgan Kaufmann.	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.	Oza, N. C., & Russell, S. (2001). Online bagging and boosting. In Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics (AISTATS 2001), pp. 105–112.
उपनाम	streaming boosting, incremental boosting, online AdaBoost, online ensemble boosting	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	incremental bagging, streaming bagging, online bootstrap aggregating, OzaBag
संबंधित	6	6	4
सारांश	Online Boosting adapts the classical boosting framework to data streams, updating an ensemble of weak learners one example at a time without storing the full dataset. The Oza-Russell formulation approximates AdaBoost's reweighting using Poisson-sampled instance counts, enabling accurate, adaptive classification in real-time or resource-constrained environments.	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.	Online Bagging is a streaming ensemble method introduced by Oza and Russell in 2001 that adapts the classical bootstrap aggregating (Bagging) framework to the online learning setting. Instead of resampling a fixed dataset, each incoming instance is fed to every base learner a Poisson(1)-distributed number of times, faithfully approximating bootstrap sampling as the stream evolves. The result is a robust, incrementally updated ensemble that can handle concept drift and continuous data arrival without storing the entire dataset.