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| Wzmocnienie× | Uczenie online× | |
|---|---|---|
| Dziedzina | Uczenie maszynowe | Uczenie maszynowe |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 1990–1997 | 1958–2000s |
| Twórca≠ | Schapire, R. E.; Freund, Y. | Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors) |
| Typ≠ | Sequential ensemble (iterative reweighting) | Learning paradigm (sequential model update) |
| Źródło pierwotne≠ | Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139. DOI ↗ | Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194. DOI ↗ |
| Inne nazwy | AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble | incremental learning, sequential learning, streaming learning, online machine learning |
| Pokrewne | 6 | 6 |
| Podsumowanie≠ | Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy. | 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. |
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