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| Semi-Supervised Learning× | Semi-supervised Random Forest× | |
|---|---|---|
| Fachgebiet | Maschinelles Lernen | Maschinelles Lernen |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 1970s–2006 (formalized) | 2009 |
| Urheber≠ | Vapnik, V. N. and others (community of researchers, 1970s–2000s) | Leistner, C., Saffari, A., Santner, J., & Bischof, H. |
| Typ≠ | Learning paradigm | Semi-supervised ensemble classifier |
| Wegweisende Quelle≠ | Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9 | Leistner, C., Saffari, A., Santner, J., & Bischof, H. (2009). Semi-supervised random forests. In Proceedings of the IEEE 12th International Conference on Computer Vision (ICCV), pp. 506–513. IEEE. DOI ↗ |
| Aliasnamen | SSL, semi-supervised machine learning, transductive learning, label-efficient learning | SSL-RF, semi-supervised forest, label-propagation random forest, self-training random forest |
| Verwandt≠ | 5 | 3 |
| Zusammenfassung≠ | 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. | Semi-supervised Random Forest (SSL-RF) extends the classic Random Forest by exploiting both labeled and unlabeled training examples. When labeling data is expensive or time-consuming, SSL-RF assigns tentative pseudo-labels to unlabeled observations through the forest itself, then retrains on the enriched dataset, progressively improving accuracy without requiring additional human annotation. |
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