Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Jifunze Mtandaoni× | Kujifunza kwa Kiasi Kidogo cha Mifano× | |
|---|---|---|
| Nyanja | Ujifunzaji wa Mashine | Ujifunzaji wa Mashine |
| Familia | Machine learning | Machine learning |
| Mwaka wa asili≠ | 1958–2000s | 2011–2017 |
| Mwanzilishi≠ | Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors) | Lake, B. M.; Vinyals, O.; Finn, C. et al. |
| Aina≠ | Learning paradigm (sequential model update) | Meta-learning / low-data learning paradigm |
| Chanzo asilia≠ | 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 ↗ |
| Majina mbadala | incremental learning, sequential learning, streaming learning, online machine learning | FSL, low-shot learning, k-shot learning, meta-learning for few examples |
| Zinazohusiana≠ | 6 | 4 |
| Muhtasari≠ | 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. |
| ScholarGateSeti ya data ↗ |
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