Methoden vergleichen
Prüfen Sie die ausgewählten Methoden nebeneinander; abweichende Zeilen sind hervorgehoben.
| Semi-Supervised Learning× | Aktives Lernen× | |
|---|---|---|
| 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) | Burr Settles |
| Typ≠ | Learning paradigm | Interactive supervised learning framework |
| Wegweisende Quelle≠ | Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9 | Settles, B. (2009). Active learning literature survey. University of Wisconsin-Madison Computer Sciences Technical Report 1648. link ↗ |
| Aliasnamen | SSL, semi-supervised machine learning, transductive learning, label-efficient learning | Query Learning, Optimal Experimental Design (ML context), Pool-Based Active Learning, Aktif Öğrenme |
| Verwandt≠ | 5 | 2 |
| 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. | Active learning is an iterative machine-learning paradigm in which a learning algorithm selectively queries an oracle — typically a human annotator — for labels on the most informative unlabeled examples. Formalized by Burr Settles in his seminal 2009 literature survey, active learning addresses the practical bottleneck of annotation cost by achieving high model accuracy with far fewer labeled examples than passive supervised learning requires. |
| ScholarGateDatensatz ↗ |
|
|