Linganisha mbinu

	Kujifunza kwa Njia ya Kufanya Kazi Mtandaoni	Kujifunza kwa Kiasi Kidogo cha Mifano
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	2000s	2011–2017
Mwanzilishi	Cesa-Bianchi, N. and others (multiple contributors)	Lake, B. M.; Vinyals, O.; Finn, C. et al.
Aina	Hybrid learning paradigm (online + active)	Meta-learning / low-data learning paradigm
Chanzo asilia	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.	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.
Majina mbadala	streaming active learning, online query-by-committee, sequential active learning, incremental active learning	FSL, low-shot learning, k-shot learning, meta-learning for few examples
Zinazohusiana	6	4
Muhtasari	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.	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.