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| Aktywne uczenie z procesem Gaussa× | Proces Gaussa× | |
|---|---|---|
| Dziedzina | Uczenie maszynowe | Uczenie maszynowe |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 1992 | 2006 (book); roots in Kriging, 1951) |
| Twórca≠ | MacKay, D. J. C. | Rasmussen, C. E. & Williams, C. K. I. |
| Typ≠ | Bayesian active learning | Probabilistic non-parametric model |
| Źródło pierwotne≠ | MacKay, D. J. C. (1992). Information-based objective functions for active data selection. Neural Computation, 4(4), 590–604. DOI ↗ | Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9 |
| Inne nazwy | GP active learning, Gaussian process active learning, GP-AL, Bayesian active learning with GP | GP, Gaussian Process Regression, GPR, Kriging |
| Pokrewne≠ | 4 | 3 |
| Podsumowanie≠ | Active Learning Gaussian Process (GP-AL) combines a Gaussian process probabilistic model with an active learning query strategy, using the GP's posterior uncertainty to select the most informative unlabeled examples for labeling. This iterative approach minimizes labeling effort while maximizing predictive accuracy, making it ideal when labeled data is scarce or expensive to obtain. | 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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