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| Стійкий Гаусівський Процес× | Гаусівський процес× | |
|---|---|---|
| Галузь | Машинне навчання | Машинне навчання |
| Родина | Machine learning | Machine learning |
| Рік появи≠ | 2011 (formal treatment); GP foundations: Rasmussen & Williams 2006 | 2006 (book); roots in Kriging, 1951) |
| Автор методу≠ | Jylanki, P.; Vanhatalo, J.; Vehtari, A. | Rasmussen, C. E. & Williams, C. K. I. |
| Тип≠ | Probabilistic non-parametric regression / classification | Probabilistic non-parametric model |
| Основоположне джерело≠ | Jylanki, P., Vanhatalo, J., & Vehtari, A. (2011). Robust Gaussian Process Regression with a Student-t Likelihood. Journal of Machine Learning Research, 12, 3227–3257. link ↗ | Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9 |
| Інші назви | Robust GP, Student-t Process, Heavy-tailed Gaussian Process, Outlier-robust GP | GP, Gaussian Process Regression, GPR, Kriging |
| Пов'язані≠ | 5 | 3 |
| Підсумок≠ | Robust Gaussian Process (Robust GP) extends the standard Gaussian Process framework by replacing the Gaussian noise likelihood with a heavy-tailed distribution — typically Student-t — so that outliers in the training data exert less influence on the learned function. It retains the full probabilistic, uncertainty-quantifying character of a standard GP while becoming far less sensitive to corrupted or anomalous observations. | 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. |
| ScholarGateНабір даних ↗ |
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