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| 준지도 학습 K-평균 (Semi-supervised K-means)× | 능동 학습× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2001–2002 | 2009 |
| 창시자≠ | Wagstaff, K. et al. (constrained); Basu, S. et al. (seeded) | Burr Settles |
| 유형≠ | Semi-supervised clustering | Interactive supervised learning framework |
| 원전≠ | 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 ↗ | Settles, B. (2009). Active learning literature survey. University of Wisconsin-Madison Computer Sciences Technical Report 1648. link ↗ |
| 별칭 | constrained K-means, seeded K-means, partially supervised K-means, SS-K-means | Query Learning, Optimal Experimental Design (ML context), Pool-Based Active Learning, Aktif Öğrenme |
| 관련≠ | 5 | 2 |
| 요약≠ | 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. | 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. |
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