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| Aktives Lernen mit K-Nächsten-Nachbarn× | Semi-supervised K-Nearest Neighbors× | |
|---|---|---|
| Fachgebiet | Maschinelles Lernen | Maschinelles Lernen |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 1951–2010 | 2002 (semi-supervised extension); 1967 (KNN base) |
| Urheber≠ | Settles, B. (active learning framework); Fix & Hodges (KNN base) | Zhu, X. & Ghahramani, Z. (label propagation); Cover, T. & Hart, P. (KNN base) |
| Typ≠ | Active learning with KNN base learner | Semi-supervised classifier / label propagation |
| Wegweisende Quelle≠ | Settles, B. (2010). Active Learning Literature Survey. Computer Sciences Technical Report 1648, University of Wisconsin-Madison. link ↗ | Zhu, X. & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University. link ↗ |
| Aliasnamen | AL-KNN, active KNN, query-based nearest neighbor learning, uncertainty-sampling KNN | SS-KNN, semi-supervised KNN, KNN label propagation, graph-based semi-supervised KNN |
| Verwandt | 4 | 4 |
| Zusammenfassung≠ | Active learning with K-nearest neighbors combines the instance-based prediction of KNN with an iterative query strategy that selects the most informative unlabeled examples for annotation. The model requests labels only for instances where neighborhood vote margins are narrowest, achieving competitive accuracy with far fewer labeled examples than fully supervised KNN on tabular data. | Semi-supervised KNN extends the classic K-nearest neighbors algorithm to exploit large pools of unlabeled data alongside a small labeled set. By building a KNN graph over all observations and propagating known labels through the graph's edges, the method infers labels for unlabeled points without requiring expensive manual annotation of every sample. |
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