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| 준지도 K-최근접 이웃 (Semi-supervised K-Nearest Neighbors)× | 준지도 학습 서포트 벡터 머신× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2002 (semi-supervised extension); 1967 (KNN base) | 1999 |
| 창시자≠ | Zhu, X. & Ghahramani, Z. (label propagation); Cover, T. & Hart, P. (KNN base) | Joachims, T. |
| 유형≠ | Semi-supervised classifier / label propagation | Semi-supervised classifier |
| 원전≠ | Zhu, X. & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University. link ↗ | Joachims, T. (1999). Transductive Inference for Text Classification using Support Vector Machines. Proceedings of the 16th International Conference on Machine Learning (ICML), 200–209. link ↗ |
| 별칭 | SS-KNN, semi-supervised KNN, KNN label propagation, graph-based semi-supervised KNN | S3VM, Transductive SVM, TSVM, Semi-SVM |
| 관련 | 4 | 4 |
| 요약≠ | 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. | Semi-supervised Support Vector Machine (S3VM) extends the classical SVM by incorporating large quantities of unlabeled data alongside a small labeled training set. It seeks a maximum-margin hyperplane that not only separates the labeled examples but also passes through low-density regions of the full data distribution, yielding better generalization when labeled samples are scarce. |
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