विधियों की तुलना करें

	ऑनलाइन फ्यू-शॉट लर्निंग	अर्ध-पर्यवेक्षित शिक्षण
क्षेत्र	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2019	1970s–2006 (formalized)
प्रवर्तक	Finn, C. et al. (online meta-learning formalization)	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
प्रकार	Online learning + meta-learning hybrid	Learning paradigm
मौलिक स्रोत	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.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
उपनाम	online meta-learning, streaming few-shot learning, continual few-shot learning, incremental few-shot learning	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
संबंधित	4	5
सारांश	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.	Semi-supervised learning (SSL) is a machine learning paradigm that trains models using a small set of labeled examples together with a much larger pool of unlabeled data. By leveraging the structure inherent in unlabeled data, SSL achieves accuracy closer to fully supervised models while requiring far fewer costly manual labels — making it practical when labeling is expensive, slow, or resource-constrained.