So sánh phương pháp

	Học máy ít mẫu trực tuyến	Học trực tuyến
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2019	1958–2000s
Người khởi xướng	Finn, C. et al. (online meta-learning formalization)	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)
Loại	Online learning + meta-learning hybrid	Learning paradigm (sequential model update)
Công trình gốc	Finn, C., Rajeswaran, A., Kakade, S., & Levine, S. (2019). Online Meta-Learning. Proceedings of the 36th International Conference on Machine Learning (ICML), PMLR 97, 1920–1930.	Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.
Tên gọi khác	online meta-learning, streaming few-shot learning, continual few-shot learning, incremental few-shot learning	incremental learning, sequential learning, streaming learning, online machine learning
Liên quan	4	6
Tóm tắt	Online Few-shot Learning combines the streaming update principle of online learning with the data-efficiency goal of few-shot learning, enabling a model to continuously adapt to new tasks or classes from only a handful of labeled examples as data arrives sequentially — without access to the full historical dataset.	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.