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| Halb-überwachtes One-Class-SVM× | Gauß-Prozess× | |
|---|---|---|
| Fachgebiet | Maschinelles Lernen | Maschinelles Lernen |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2001–2004 | 2006 (book); roots in Kriging, 1951) |
| Urheber≠ | Extension of Scholkopf et al. (2001); semi-supervised variants studied ca. 2004–2010 | Rasmussen, C. E. & Williams, C. K. I. |
| Typ≠ | Semi-supervised anomaly / novelty detection | Probabilistic non-parametric model |
| Wegweisende Quelle≠ | Munoz, A. & Muruzabal, J. (2004). Self-Organising Maps for Outlier Detection. Neurocomputing, 58–60, 953–956. link ↗ | Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9 |
| Aliasnamen | SS-OCSVM, semi-supervised OC-SVM, semi-supervised novelty detection SVM, transductive one-class SVM | GP, Gaussian Process Regression, GPR, Kriging |
| Verwandt≠ | 5 | 3 |
| Zusammenfassung≠ | Semi-supervised One-class SVM extends the classic One-class SVM anomaly detector by incorporating unlabeled observations alongside a small set of known normal examples. The unlabeled data helps the model learn a tighter, more informative decision boundary in feature space, reducing false positives and improving anomaly recall compared to the purely unsupervised baseline. | 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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