Sammenlign metoder

	Online Support Vector Machine	Online logistisk regression
Fagområde	Maskinlæring	Maskinlæring
Familie	Machine learning	Machine learning
Oprindelsesår	2005–2011	1960s (perceptron); formalized for logistic loss ~2000s
Ophavsperson	Shalev-Shwartz, Singer, et al. (Pegasos); Bordes, Bottou et al. (LASVM)	Rosenblatt, F. / Widrow, B. (perceptron era); modern SGD form: Bottou, L.
Type	Online kernel classifier	Incremental supervised classifier
Oprindelig kilde	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.
Aliasser	Online SVM, Incremental SVM, LASVM, Pegasos SVM	incremental logistic regression, streaming logistic regression, SGD logistic classifier, online binary classifier
Relaterede	3	5
Resumé	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.