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| Uczenie przyrostowe zespołowe (Ensemble Online Learning)× | Uczenie ze wsparciem częściowym× | |
|---|---|---|
| Dziedzina | Uczenie maszynowe | Uczenie maszynowe |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 2001 | 1970s–2006 (formalized) |
| Twórca≠ | Oza, N. C. & Russell, S. | Vapnik, V. N. and others (community of researchers, 1970s–2000s) |
| Typ≠ | Ensemble (online / incremental) | Learning paradigm |
| Źródło pierwotne≠ | Oza, N. C., & Russell, S. (2001). Online bagging and boosting. In Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics (AISTATS 2001), pp. 229–236. link ↗ | Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9 |
| Inne nazwy | online ensemble methods, streaming ensemble learning, incremental ensemble learning, adaptive ensemble learning | SSL, semi-supervised machine learning, transductive learning, label-efficient learning |
| Pokrewne≠ | 6 | 5 |
| Podsumowanie≠ | Ensemble Online Learning combines multiple base learners that are trained incrementally on a stream of data, updating each model one observation at a time. By aggregating the predictions of diverse online learners, the ensemble achieves accuracy and robustness that surpass any single incremental model, while adapting continuously to changing data distributions. | 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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