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| ベイジアン・ガウス過程× | ベイズ最適化× | |
|---|---|---|
| 分野≠ | 機械学習 | 最適化 |
| 系統≠ | Machine learning | Process / pipeline |
| 提唱年≠ | 1978–2006 | 1975 (foundational); 2012 (ML standard) |
| 提唱者≠ | O'Hagan, A.; Neal, R. M.; Rasmussen, C. E. & Williams, C. K. I. | Mockus (1975); popularised for ML by Snoek, Larochelle & Adams (2012) |
| 種類≠ | Probabilistic kernel model | Sequential model-based black-box optimization |
| 原典≠ | Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9 | Snoek, J., Larochelle, H., & Adams, R.P. (2012). Practical Bayesian Optimization of Machine Learning Algorithms. Advances in Neural Information Processing Systems (NeurIPS), 25. link ↗ |
| 別名 | GP regression, GPR, Gaussian process model, GP classifier | Bayesçi Optimizasyon (Hyperparameter Tuning), surrogate-based optimization, sequential model-based optimization, SMBO |
| 関連≠ | 3 | 2 |
| 概要≠ | 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. | Bayesian Optimization is a sequential, model-based strategy for finding the optimum of expensive black-box functions with as few evaluations as possible. Rooted in the work of Mockus (1975) and brought to mainstream machine-learning practice by Snoek, Larochelle, and Adams (2012), it fits a probabilistic surrogate model — typically a Gaussian Process — to past observations and uses an acquisition function to decide where to probe next, balancing exploration of unknown regions with exploitation of promising ones. |
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