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| Samouczenie z wykorzystaniem lasów losowych× | Uczenie ze wsparciem częściowym× | |
|---|---|---|
| Dziedzina | Uczenie maszynowe | Uczenie maszynowe |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 2012–2022 | 1970s–2006 (formalized) |
| Twórca≠ | Lefortier, D. et al.; Criminisi, A. et al. (semi-supervised RF lineage) | Vapnik, V. N. and others (community of researchers, 1970s–2000s) |
| Typ≠ | Semi-supervised ensemble (self-supervised pretext task + RF) | Learning paradigm |
| Źródło pierwotne≠ | Lefortier, D., Chitta, K., & Agrawal, P. (2022). Self-supervised random forests. arXiv:2204.01430. link ↗ | Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9 |
| Inne nazwy | SSL-RF, self-supervised RF, self-supervised ensemble forest, unsupervised random forest with self-labeling | SSL, semi-supervised machine learning, transductive learning, label-efficient learning |
| Pokrewne≠ | 6 | 5 |
| Podsumowanie≠ | Self-supervised Random Forest (SSL-RF) extends the classic random forest to settings where labeled examples are scarce. The forest is first trained using automatically generated pseudo-labels derived from a self-supervised pretext task — such as predicting data transformations or masked features — and then refined on whatever true labels are available, marrying the label-efficiency of self-supervised learning with the robustness of ensemble trees. | 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. |
| ScholarGateZbiór danych ↗ |
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