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| 正則化ガウス過程× | 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 (canonical formulation); kernel regularization roots 1990s | 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 kernel model with regularization | 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 | Regularized GP, GP with noise regularization, sparse regularized Gaussian process, regularized Gaussian process regression | GP regression, GPR, Gaussian process model, GP classifier |
| 関連≠ | 4 | 3 |
| 概要≠ | A Regularized Gaussian Process (GP) is a probabilistic kernel-based model that places a prior over functions and explicitly controls overfitting through a noise regularization parameter — the observation noise variance — that prevents the model from memorizing training labels. It produces calibrated uncertainty estimates alongside predictions, making it uniquely suited to small or expensive datasets where knowing how confident the model is matters as much as the prediction itself. | 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. |
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