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| Active Learning One-class SVM× | SVM Kelas Tunggal× | |
|---|---|---|
| Bidang | Pembelajaran Mesin | Pembelajaran Mesin |
| Keluarga | Machine learning | Machine learning |
| Tahun asal≠ | 2000s | 1999–2001 |
| Pengasas≠ | Schölkopf et al. (OCSVM); active variant developed in the anomaly-detection literature (2000s–2010s) | Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C. |
| Jenis≠ | Semi-supervised anomaly/novelty detection with iterative labeling | Anomaly / novelty detection (unsupervised) |
| Sumber perintis≠ | Schölkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (1999). Estimating the Support of a High-Dimensional Distribution. Neural Computation, 13(7), 1443–1471. DOI ↗ | 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 ↗ |
| Alias | AL-OCSVM, active one-class SVM, active novelty detection SVM, query-driven OCSVM | OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM |
| Berkaitan≠ | 4 | 3 |
| Ringkasan≠ | Active Learning One-class SVM combines the one-class support vector machine — a kernel-based novelty detector that learns the boundary of normal data — with an active learning loop that selects the most informative unlabeled instances for expert annotation. The result is a data-efficient anomaly detector that improves its decision boundary with minimal labeling effort. | 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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