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| Ενεργή Μάθηση με Γκαουσιανή Διαδικασία× | Διαδικασία Γκάους× | |
|---|---|---|
| Πεδίο | Μηχανική Μάθηση | Μηχανική Μάθηση |
| Οικογένεια | Machine learning | Machine learning |
| Έτος προέλευσης≠ | 1992 | 2006 (book); roots in Kriging, 1951) |
| Δημιουργός≠ | MacKay, D. J. C. | Rasmussen, C. E. & Williams, C. K. I. |
| Τύπος≠ | Bayesian active learning | Probabilistic non-parametric model |
| Θεμελιώδης πηγή≠ | 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 |
| Εναλλακτικές ονομασίες | GP active learning, Gaussian process active learning, GP-AL, Bayesian active learning with GP | GP, Gaussian Process Regression, GPR, Kriging |
| Συναφείς≠ | 4 | 3 |
| Σύνοψη≠ | 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. |
| ScholarGateΣύνολο δεδομένων ↗ |
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