পদ্ধতির তুলনা করুন

	বেয়েশীয় আধা-তত্ত্বাবধানাধীন শিখন (Bayesian Semi-supervised Learning)	ফিউ-শট লার্নিং
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2003–2006	2011–2017
প্রবর্তক	Chapelle, Scholkopf & Zien; Zhu, Ghahramani & Lafferty	Lake, B. M.; Vinyals, O.; Finn, C. et al.
ধরন	Probabilistic semi-supervised framework	Meta-learning / low-data learning paradigm
মৌলিক উৎস	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.). (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9	Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., & Kavukcuoglu, K. (2016). Matching Networks for One Shot Learning. Advances in Neural Information Processing Systems (NeurIPS), 29.
অপর নাম	Bayesian SSL, probabilistic semi-supervised learning, generative semi-supervised model, Bayesian transductive learning	FSL, low-shot learning, k-shot learning, meta-learning for few examples
সম্পর্কিত	6	4
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.	Few-shot learning is a machine learning paradigm that trains models to recognize new classes or solve new tasks from only a handful of labeled examples — typically one to five — by leveraging prior knowledge acquired from a large, related training distribution. It is especially relevant in domains where labeling is expensive, scarce, or structurally limited.