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| SVM univarié auto-supervisé× | SVM univarié semi-supervisé× | |
|---|---|---|
| Domaine | Apprentissage automatique | Apprentissage automatique |
| Famille | Machine learning | Machine learning |
| Année d'origine≠ | 2018 | 2001–2004 |
| Auteur d'origine≠ | Golan & El-Yaniv; Ruff et al. | Extension of Scholkopf et al. (2001); semi-supervised variants studied ca. 2004–2010 |
| Type≠ | Self-supervised anomaly/novelty detection | Semi-supervised anomaly / novelty detection |
| Source fondatrice≠ | Golan, I. & El-Yaniv, R. (2018). Deep One-Class Classification. Proceedings of the 35th International Conference on Machine Learning (ICML), PMLR 80, 1747–1756. link ↗ | Munoz, A. & Muruzabal, J. (2004). Self-Organising Maps for Outlier Detection. Neurocomputing, 58–60, 953–956. link ↗ |
| Alias | SS-OCSVM, Self-supervised SVDD, Self-supervised novelty detection, Pretext-task OC-SVM | SS-OCSVM, semi-supervised OC-SVM, semi-supervised novelty detection SVM, transductive one-class SVM |
| Apparentées≠ | 6 | 5 |
| Résumé≠ | Self-supervised One-class SVM combines pretext-task-based representation learning with One-class SVM to detect anomalies and novelties without requiring labeled anomaly examples. The model first learns expressive feature embeddings from normal data alone, then fits an OC-SVM boundary in the learned feature space to flag out-of-distribution samples. | 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. |
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