Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Bayesovská víceobjektivní optimalizace× | Bayesovská optimalizace× | |
|---|---|---|
| Obor≠ | Simulace | Optimalizace |
| Rodina | Process / pipeline | Process / pipeline |
| Rok vzniku≠ | 2006-2016 | 1975 (foundational); 2012 (ML standard) |
| Tvůrce≠ | Emmerich, M.; Svenson, J.; and related Gaussian process optimization community | Mockus (1975); popularised for ML by Snoek, Larochelle & Adams (2012) |
| Typ≠ | Surrogate-model-assisted multi-objective optimizer | Sequential model-based black-box optimization |
| Původní zdroj≠ | Svenson, J., Santner, T. (2016). Multiobjective optimization of expensive-to-evaluate deterministic computer simulator models. Computational Statistics & Data Analysis, 94, 250-264. DOI ↗ | Snoek, J., Larochelle, H., & Adams, R.P. (2012). Practical Bayesian Optimization of Machine Learning Algorithms. Advances in Neural Information Processing Systems (NeurIPS), 25. link ↗ |
| Další názvy | BMOO, Bayesian MOO, Multi-objective Bayesian optimization, MOBO | Bayesçi Optimizasyon (Hyperparameter Tuning), surrogate-based optimization, sequential model-based optimization, SMBO |
| Příbuzné≠ | 3 | 2 |
| Shrnutí≠ | Bayesian Multi-Objective Optimization (BMOO/MOBO) uses Gaussian process surrogate models to approximate multiple expensive objective functions and guides the search toward the Pareto frontier with minimal real evaluations. By quantifying prediction uncertainty at each candidate point, it balances exploration of unknown regions against exploitation of promising solutions, making it especially powerful when each function evaluation is computationally or experimentally costly. | 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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