পদ্ধতির তুলনা করুন

	বেয়েসীয় অনলাইন লার্নিং	আধা-তত্ত্বাবধানাধীন শিখন (Semi-supervised Learning)
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	1990s–2000s	1970s–2006 (formalized)
প্রবর্তক	Opper, M.; Sato, M. (among key contributors)	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
ধরন	Probabilistic sequential learning	Learning paradigm
মৌলিক উৎস	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
অপর নাম	online Bayesian inference, sequential Bayesian learning, recursive Bayesian estimation, BOL	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
সম্পর্কিত	6	5
সারসংক্ষেপ	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.