방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 자기 지도 학습 기반 단일 클래스 SVM× | 준지도 학습 단일 클래스 SVM (Semi-supervised One-class SVM)× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2018 | 2001–2004 |
| 창시자≠ | Golan & El-Yaniv; Ruff et al. | Extension of Scholkopf et al. (2001); semi-supervised variants studied ca. 2004–2010 |
| 유형≠ | Self-supervised anomaly/novelty detection | Semi-supervised anomaly / novelty detection |
| 원전≠ | 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 ↗ |
| 별칭 | 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 |
| 관련≠ | 6 | 5 |
| 요약≠ | 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. |
| ScholarGate데이터셋 ↗ |
|
|