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| 준지도 가우시안 프로세스× | 준지도학습 랜덤 포레스트× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2004 | 2009 |
| 창시자≠ | Lawrence, N. D. & Jordan, M. I. | Leistner, C., Saffari, A., Santner, J., & Bischof, H. |
| 유형≠ | Probabilistic model (semi-supervised) | Semi-supervised ensemble classifier |
| 원전≠ | Lawrence, N. D., & Jordan, M. I. (2004). Semi-supervised learning via Gaussian processes. In Advances in Neural Information Processing Systems (NIPS), 17, 753–760. MIT Press. link ↗ | Leistner, C., Saffari, A., Santner, J., & Bischof, H. (2009). Semi-supervised random forests. In Proceedings of the IEEE 12th International Conference on Computer Vision (ICCV), pp. 506–513. IEEE. DOI ↗ |
| 별칭 | SS-GP, semi-supervised GP, Gaussian process with unlabeled data, GP manifold learning | SSL-RF, semi-supervised forest, label-propagation random forest, self-training random forest |
| 관련≠ | 5 | 3 |
| 요약≠ | Semi-supervised Gaussian Process extends the probabilistic GP framework to exploit unlabeled data alongside a small set of labeled observations. By placing a GP prior over functions and leveraging the geometric structure revealed by unlabeled inputs, it learns more accurate and better-calibrated predictors than a purely supervised GP when labels are scarce, making it well suited for scientific and medical problems where annotation is expensive. | Semi-supervised Random Forest (SSL-RF) extends the classic Random Forest by exploiting both labeled and unlabeled training examples. When labeling data is expensive or time-consuming, SSL-RF assigns tentative pseudo-labels to unlabeled observations through the forest itself, then retrains on the enriched dataset, progressively improving accuracy without requiring additional human annotation. |
| ScholarGate데이터셋 ↗ |
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