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| Semi-supervised Gaussian Process× | Gauß-Prozess× | |
|---|---|---|
| Fachgebiet | Maschinelles Lernen | Maschinelles Lernen |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2004 | 2006 (book); roots in Kriging, 1951) |
| Urheber≠ | Lawrence, N. D. & Jordan, M. I. | Rasmussen, C. E. & Williams, C. K. I. |
| Typ≠ | Probabilistic model (semi-supervised) | Probabilistic non-parametric model |
| Wegweisende Quelle≠ | Lawrence, N. D., & Jordan, M. I. (2004). Semi-supervised learning via Gaussian processes. In Advances in Neural Information Processing Systems (NIPS), 17, 753–760. MIT Press. link ↗ | Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9 |
| Aliasnamen | SS-GP, semi-supervised GP, Gaussian process with unlabeled data, GP manifold learning | GP, Gaussian Process Regression, GPR, Kriging |
| Verwandt≠ | 5 | 3 |
| Zusammenfassung≠ | Semi-supervised Gaussian Process extends the probabilistic GP framework to exploit unlabeled data alongside a small set of labeled observations. By placing a GP prior over functions and leveraging the geometric structure revealed by unlabeled inputs, it learns more accurate and better-calibrated predictors than a purely supervised GP when labels are scarce, making it well suited for scientific and medical problems where annotation is expensive. | 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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