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| Uczenie online× | Uczenie ze wsparciem częściowym× | |
|---|---|---|
| Dziedzina | Uczenie maszynowe | Uczenie maszynowe |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 1958–2000s | 1970s–2006 (formalized) |
| Twórca≠ | Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors) | Vapnik, V. N. and others (community of researchers, 1970s–2000s) |
| Typ≠ | Learning paradigm (sequential model update) | Learning paradigm |
| Źródło pierwotne≠ | Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194. DOI ↗ | Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9 |
| Inne nazwy | incremental learning, sequential learning, streaming learning, online machine learning | SSL, semi-supervised machine learning, transductive learning, label-efficient learning |
| Pokrewne≠ | 6 | 5 |
| Podsumowanie≠ | 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. | 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. |
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