手法を比較
選択した手法を並べて確認できます。異なる行はハイライト表示されます。
| 半教師ありガウス過程× | ベイジアン・ガウス過程× | |
|---|---|---|
| 分野 | 機械学習 | 機械学習 |
| 系統 | Machine learning | Machine learning |
| 提唱年≠ | 2004 | 1978–2006 |
| 提唱者≠ | Lawrence, N. D. & Jordan, M. I. | O'Hagan, A.; Neal, R. M.; Rasmussen, C. E. & Williams, C. K. I. |
| 種類≠ | Probabilistic model (semi-supervised) | Probabilistic kernel model |
| 原典≠ | 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 ↗ | Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9 |
| 別名 | SS-GP, semi-supervised GP, Gaussian process with unlabeled data, GP manifold learning | GP regression, GPR, Gaussian process model, GP classifier |
| 関連≠ | 5 | 3 |
| 概要≠ | 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. | A Bayesian Gaussian Process (GP) places a probability distribution directly over functions, using a kernel to encode similarity between inputs. After observing data, Bayes' rule converts this prior into a posterior that yields not just point predictions but calibrated uncertainty estimates at every new input — making it one of the most principled probabilistic models in machine learning. |
| ScholarGateデータセット ↗ |
|
|