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| Uczące się zespoły w uczeniu ze wsparciem częściowym× | Uczenie ze wsparciem częściowym× | |
|---|---|---|
| Dziedzina | Uczenie maszynowe | Uczenie maszynowe |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 1998–2005 | 1970s–2006 (formalized) |
| Twórca≠ | Blum & Mitchell (co-training); Zhou & Li (tri-training) | Vapnik, V. N. and others (community of researchers, 1970s–2000s) |
| Typ≠ | Ensemble + semi-supervised hybrid paradigm | Learning paradigm |
| Źródło pierwotne≠ | Zhou, Z.-H., & Li, M. (2005). Tri-training: Exploiting unlabeled data using three classifiers. IEEE Transactions on Knowledge and Data Engineering, 17(11), 1529–1541. DOI ↗ | Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9 |
| Inne nazwy | semi-supervised ensemble, SSL ensemble, ensemble-based SSL, co-training ensemble | SSL, semi-supervised machine learning, transductive learning, label-efficient learning |
| Pokrewne≠ | 6 | 5 |
| Podsumowanie≠ | Ensemble semi-supervised learning combines multiple base learners with the semi-supervised paradigm, exploiting both a small labeled set and a large pool of unlabeled data. By letting diverse classifiers teach each other through pseudo-labeling or co-training, the ensemble improves generalization far beyond what either approach alone could achieve with limited labels. | 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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