विधियों की तुलना करें

	ऑनलाइन लर्निंग	फ्यू-शॉट लर्निंग
क्षेत्र	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	1958–2000s	2011–2017
प्रवर्तक	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)	Lake, B. M.; Vinyals, O.; Finn, C. et al.
प्रकार	Learning paradigm (sequential model update)	Meta-learning / low-data learning paradigm
मौलिक स्रोत	Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.	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.
उपनाम	incremental learning, sequential learning, streaming learning, online machine learning	FSL, low-shot learning, k-shot learning, meta-learning for few examples
संबंधित	6	4
सारांश	Online learning is a machine learning paradigm in which a model is updated incrementally as each new data point arrives, rather than being trained once on a fixed dataset. It is essential when data streams continuously, storage is limited, or the underlying distribution shifts over time. Theoretical performance is measured by cumulative regret relative to the best fixed predictor in hindsight.	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.