Comparer des méthodes

	Machine à vecteurs de support en ligne	Apprentissage en ligne
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2005–2011	1958–2000s
Auteur d'origine	Shalev-Shwartz, Singer, et al. (Pegasos); Bordes, Bottou et al. (LASVM)	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)
Type	Online kernel classifier	Learning paradigm (sequential model update)
Source fondatrice	Shalev-Shwartz, S., Singer, Y., Srebro, N., & Cotter, A. (2011). Pegasos: Primal estimated sub-gradient solver for SVM. Mathematical Programming, 127(1), 3–30.	Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.
Alias	Online SVM, Incremental SVM, LASVM, Pegasos SVM	incremental learning, sequential learning, streaming learning, online machine learning
Apparentées	3	6
Résumé	Online SVM adapts the classical support vector machine to streaming or sequentially arriving data by updating the decision boundary one example at a time rather than solving a global quadratic program. Algorithms such as Pegasos and LASVM make this tractable at large scale, preserving the margin-maximising spirit of SVMs with sub-linear time per update.	Online learning is a machine learning paradigm in which a model is updated incrementally as each new data point arrives, rather than being trained once on a fixed dataset. It is essential when data streams continuously, storage is limited, or the underlying distribution shifts over time. Theoretical performance is measured by cumulative regret relative to the best fixed predictor in hindsight.