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。
| 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≠ | 2006 (book); roots in Kriging, 1951) | 1978–2006 |
| 提唱者≠ | Rasmussen, C. E. & Williams, C. K. I. | O'Hagan, A.; Neal, R. M.; Rasmussen, C. E. & Williams, C. K. I. |
| 種類≠ | Probabilistic non-parametric model | Probabilistic kernel model |
| 原典 | Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9 | Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9 |
| 別名 | GP, Gaussian Process Regression, GPR, Kriging | GP regression, GPR, Gaussian process model, GP classifier |
| 関連 | 3 | 3 |
| 概要≠ | A Gaussian Process (GP) is a non-parametric, fully probabilistic machine learning model that places a prior distribution directly over functions. Rather than predicting a single value, it returns a predictive mean and a calibrated uncertainty estimate at every test point, making it especially valuable for regression on small to medium datasets and for Bayesian optimization tasks. | 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 ↗ |
|
|