পদ্ধতির তুলনা করুন

	বেয়েশীয় আধা-তত্ত্বাবধানাধীন শিখন (Bayesian Semi-supervised Learning)	ট্রান্সফার লার্নিং
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2003–2006	2010 (formalized); 1990s (early roots)
প্রবর্তক	Chapelle, Scholkopf & Zien; Zhu, Ghahramani & Lafferty	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
ধরন	Probabilistic semi-supervised framework	Learning paradigm
মৌলিক উৎস	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.). (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
অপর নাম	Bayesian SSL, probabilistic semi-supervised learning, generative semi-supervised model, Bayesian transductive learning	TL, domain adaptation, fine-tuning, pre-trained model adaptation
সম্পর্কিত	6	3
সারসংক্ষেপ	Bayesian semi-supervised learning is a probabilistic framework that uses both a small labeled dataset and a larger pool of unlabeled observations to infer model parameters and make predictions. By treating missing labels as latent variables and placing priors over parameters, it naturally quantifies uncertainty while leveraging unlabeled data to improve generalization.	Transfer learning is a machine learning paradigm in which knowledge gained from training a model on a source task or domain is reused to improve learning on a different but related target task or domain. It is especially powerful when labeled data for the target task is scarce, and it underlies most modern deep learning applications in computer vision, natural language processing, and beyond.