Σύγκριση μεθόδων

	Διαδικτυακή Μπεϋζιανή Μάθηση	Ημι-επιβλεπόμενη Μάθηση
Πεδίο	Μηχανική Μάθηση	Μηχανική Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	1990s–2000s	1970s–2006 (formalized)
Δημιουργός	Opper, M.; Sato, M. (among key contributors)	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Τύπος	Probabilistic sequential learning	Learning paradigm
Θεμελιώδης πηγή	Opper, M. (1998). A Bayesian approach to on-line learning. In D. Saad (Ed.), On-Line Learning in Neural Networks (pp. 363–378). Cambridge University Press.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
Εναλλακτικές ονομασίες	online Bayesian inference, sequential Bayesian learning, recursive Bayesian estimation, BOL	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
Συναφείς	6	5
Σύνοψη	Bayesian online learning applies Bayesian inference sequentially: each time a new observation arrives, the current posterior over model parameters becomes the prior for the next update. The result is a principled probabilistic framework that maintains calibrated uncertainty estimates throughout, making it well-suited for streaming and non-stationary data settings.	Semi-supervised learning (SSL) is a machine learning paradigm that trains models using a small set of labeled examples together with a much larger pool of unlabeled data. By leveraging the structure inherent in unlabeled data, SSL achieves accuracy closer to fully supervised models while requiring far fewer costly manual labels — making it practical when labeling is expensive, slow, or resource-constrained.