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| Aktywne uczenie częściowo nadzorowane× | Uczenie ze wsparciem częściowym× | |
|---|---|---|
| Dziedzina | Uczenie maszynowe | Uczenie maszynowe |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 2002 | 1970s–2006 (formalized) |
| Twórca≠ | Muslea, I., Minton, S., & Knoblock, C. A. | Vapnik, V. N. and others (community of researchers, 1970s–2000s) |
| Typ≠ | Hybrid learning framework | Learning paradigm |
| Źródło pierwotne≠ | Settles, B. (2012). Active Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool. DOI ↗ | Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9 |
| Inne nazwy | SSAL, active semi-supervised learning, query-based semi-supervised learning, semi-supervised learning with active queries | SSL, semi-supervised machine learning, transductive learning, label-efficient learning |
| Pokrewne≠ | 3 | 5 |
| Podsumowanie≠ | Semi-supervised Active Learning (SSAL) is a hybrid learning paradigm that combines active learning's selective query strategy with semi-supervised learning's ability to exploit unlabeled data. The model iteratively selects the most informative unlabeled instances for expert annotation while simultaneously leveraging the large pool of unannotated samples to improve its own representations, dramatically reducing labeling costs while maintaining strong predictive accuracy. | 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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