השוואת שיטות

	Support Vector Machine מקוון (Online Support Vector Machine)	למידה מקוונת
תחום	למידת מכונה	למידת מכונה
משפחה	Machine learning	Machine learning
שנת המקור	2005–2011	1958–2000s
הוגה השיטה	Shalev-Shwartz, Singer, et al. (Pegasos); Bordes, Bottou et al. (LASVM)	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)
סוג	Online kernel classifier	Learning paradigm (sequential model update)
מקור מכונן	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.
כינויים	Online SVM, Incremental SVM, LASVM, Pegasos SVM	incremental learning, sequential learning, streaming learning, online machine learning
קשורות	3	6
תקציר	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.