Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Aprendizaje en línea× | Aprendizaje con Pocos Ejemplos× | |
|---|---|---|
| Campo | Aprendizaje automático | Aprendizaje automático |
| Familia | Machine learning | Machine learning |
| Año de origen≠ | 1958–2000s | 2011–2017 |
| Autor original≠ | Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors) | Lake, B. M.; Vinyals, O.; Finn, C. et al. |
| Tipo≠ | Learning paradigm (sequential model update) | Meta-learning / low-data learning paradigm |
| Fuente seminal≠ | Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194. DOI ↗ | 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. link ↗ |
| Alias | incremental learning, sequential learning, streaming learning, online machine learning | FSL, low-shot learning, k-shot learning, meta-learning for few examples |
| Relacionados≠ | 6 | 4 |
| Resumen≠ | 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. |
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