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| 준지도 학습 단일 클래스 SVM (Semi-supervised One-class SVM)× | One-Class SVM× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2001–2004 | 1999–2001 |
| 창시자≠ | Extension of Scholkopf et al. (2001); semi-supervised variants studied ca. 2004–2010 | Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C. |
| 유형≠ | Semi-supervised anomaly / novelty detection | Anomaly / novelty detection (unsupervised) |
| 원전≠ | Munoz, A. & Muruzabal, J. (2004). Self-Organising Maps for Outlier Detection. Neurocomputing, 58–60, 953–956. link ↗ | Scholkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001). Estimating the support of a high-dimensional distribution. Neural Computation, 13(7), 1443–1471. DOI ↗ |
| 별칭 | SS-OCSVM, semi-supervised OC-SVM, semi-supervised novelty detection SVM, transductive one-class SVM | OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM |
| 관련≠ | 5 | 3 |
| 요약≠ | 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. | One-class SVM is an unsupervised anomaly and novelty detection algorithm that learns a tight boundary around normal training data in a kernel-induced feature space, flagging new observations that fall outside that boundary as outliers. Introduced by Scholkopf et al. in 1999–2001, it extends the SVM framework to the single-class setting where no labelled anomalies are available. |
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