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| 베이지안 최적화× | 가우시안 프로세스× | |
|---|---|---|
| 분야≠ | 최적화 | 머신러닝 |
| 계열≠ | Process / pipeline | Machine learning |
| 기원 연도≠ | 1975 (foundational); 2012 (ML standard) | 2006 (book); roots in Kriging, 1951) |
| 창시자≠ | Mockus (1975); popularised for ML by Snoek, Larochelle & Adams (2012) | Rasmussen, C. E. & Williams, C. K. I. |
| 유형≠ | Sequential model-based black-box optimization | Probabilistic non-parametric model |
| 원전≠ | Snoek, J., Larochelle, H., & Adams, R.P. (2012). Practical Bayesian Optimization of Machine Learning Algorithms. Advances in Neural Information Processing Systems (NeurIPS), 25. link ↗ | Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9 |
| 별칭 | Bayesçi Optimizasyon (Hyperparameter Tuning), surrogate-based optimization, sequential model-based optimization, SMBO | GP, Gaussian Process Regression, GPR, Kriging |
| 관련≠ | 2 | 3 |
| 요약≠ | 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. | 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. |
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