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| Semi-überwachtes K-Means× | Semi-Supervised Learning× | |
|---|---|---|
| Fachgebiet | Maschinelles Lernen | Maschinelles Lernen |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2001–2002 | 1970s–2006 (formalized) |
| Urheber≠ | Wagstaff, K. et al. (constrained); Basu, S. et al. (seeded) | Vapnik, V. N. and others (community of researchers, 1970s–2000s) |
| Typ≠ | Semi-supervised clustering | Learning paradigm |
| Wegweisende Quelle≠ | Wagstaff, K., Cardie, C., Rogers, S., & Schroedl, S. (2001). Constrained K-means Clustering with Background Knowledge. In Proceedings of the 18th International Conference on Machine Learning (ICML 2001), pp. 577–584. link ↗ | Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9 |
| Aliasnamen | constrained K-means, seeded K-means, partially supervised K-means, SS-K-means | SSL, semi-supervised machine learning, transductive learning, label-efficient learning |
| Verwandt | 5 | 5 |
| Zusammenfassung≠ | Semi-supervised K-means extends standard K-means clustering by incorporating partial supervision — either a small set of labeled seed points or pairwise must-link and cannot-link constraints — to guide cluster formation. It bridges unsupervised clustering and fully supervised classification, enabling more meaningful clusters when labels are scarce but costly to obtain in full. | 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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