方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 在线学习× | 少样本学习× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 1958–2000s | 2011–2017 |
| 提出者≠ | Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors) | Lake, B. M.; Vinyals, O.; Finn, C. et al. |
| 类型≠ | Learning paradigm (sequential model update) | Meta-learning / low-data learning paradigm |
| 开创性文献≠ | 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 ↗ |
| 别名 | incremental learning, sequential learning, streaming learning, online machine learning | FSL, low-shot learning, k-shot learning, meta-learning for few examples |
| 相关≠ | 6 | 4 |
| 摘要≠ | 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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