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| 준지도 선형 회귀 (Semi-supervised Linear Regression)× | 레이블 전파× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2005–2006 | 2002 |
| 창시자≠ | Chapelle, O.; Scholkopf, B.; Zien, A. (seminal synthesis); Zhou & Li (co-training formulation) | Zhu, X. & Ghahramani, Z. |
| 유형≠ | Semi-supervised regression model | Graph-based semi-supervised classification |
| 원전≠ | Chapelle, O., Scholkopf, B., & Zien, A. (Eds.). (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9 | Zhu, X., & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University. link ↗ |
| 별칭 | SSL linear regression, semi-supervised least squares, transductive linear regression, label-efficient linear regression | LP, label spreading, graph-based semi-supervised learning, harmonic label propagation |
| 관련≠ | 4 | 3 |
| 요약≠ | Semi-supervised linear regression fits a linear model on a small labeled dataset and then leverages a larger pool of unlabeled observations to improve coefficient estimates and generalization. By generating pseudo-labels for unlabeled points and iteratively refining the model, it achieves better predictive accuracy than a purely supervised linear model trained on scarce labels alone. | Label Propagation is a graph-based semi-supervised learning algorithm introduced by Zhu and Ghahramani in 2002 that spreads class labels from a small set of labeled nodes to a large set of unlabeled nodes by iteratively diffusing label information along the edges of a similarity graph, exploiting the manifold structure of the data. |
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