Σύγκριση μεθόδων

	Διαδικτυακή Μηχανή Υποστήριξης Διανυσμάτων (Online Support Vector Machine)	Λογιστική Παλινδρόμηση σε Συνεχή Ροή (Online Logistic Regression)
Πεδίο	Μηχανική Μάθηση	Μηχανική Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2005–2011	1960s (perceptron); formalized for logistic loss ~2000s
Δημιουργός	Shalev-Shwartz, Singer, et al. (Pegasos); Bordes, Bottou et al. (LASVM)	Rosenblatt, F. / Widrow, B. (perceptron era); modern SGD form: Bottou, L.
Τύπος	Online kernel classifier	Incremental supervised classifier
Θεμελιώδης πηγή	Shalev-Shwartz, S., Singer, Y., Srebro, N., & Cotter, A. (2011). Pegasos: Primal estimated sub-gradient solver for SVM. Mathematical Programming, 127(1), 3–30.	Bottou, L. (2010). Large-Scale Machine Learning with Stochastic Gradient Descent. In Proceedings of COMPSTAT 2010, 177–186. Physica-Verlag.
Εναλλακτικές ονομασίες	Online SVM, Incremental SVM, LASVM, Pegasos SVM	incremental logistic regression, streaming logistic regression, SGD logistic classifier, online binary classifier
Συναφείς	3	5
Σύνοψη	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 Logistic Regression fits a logistic classifier one sample (or mini-batch) at a time via stochastic gradient descent, updating model weights as each observation arrives rather than waiting to see the full dataset. This makes it the standard choice for high-volume, streaming, or memory-constrained binary classification problems where batch training is infeasible.