Σύγκριση μεθόδων

	Online Active Learning	Λογιστική Παλινδρόμηση σε Συνεχή Ροή (Online Logistic Regression)
Πεδίο	Μηχανική Μάθηση	Μηχανική Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2000s	1960s (perceptron); formalized for logistic loss ~2000s
Δημιουργός	Cesa-Bianchi, N. and others (multiple contributors)	Rosenblatt, F. / Widrow, B. (perceptron era); modern SGD form: Bottou, L.
Τύπος	Hybrid learning paradigm (online + active)	Incremental supervised classifier
Θεμελιώδης πηγή	Cesa-Bianchi, N., Gentile, C., & Zaniboni, L. (2006). Worst-case analysis of selective sampling for linear classification. Journal of Machine Learning Research, 7, 1205–1230.	Bottou, L. (2010). Large-Scale Machine Learning with Stochastic Gradient Descent. In Proceedings of COMPSTAT 2010, 177–186. Physica-Verlag.
Εναλλακτικές ονομασίες	streaming active learning, online query-by-committee, sequential active learning, incremental active learning	incremental logistic regression, streaming logistic regression, SGD logistic classifier, online binary classifier
Συναφείς	6	5
Σύνοψη	Online active learning combines two complementary paradigms: it processes data as a stream (online learning) and selectively requests labels only for the most informative instances (active learning). The result is a model that adapts continuously to new data while keeping labeling costs low — useful whenever labeled data is expensive and examples arrive sequentially rather than all at once.	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.