قارن الطرق

	اكتشاف الشذوذ باستخدام المشفر التلقائي والتعلم النشط	التعلم النشط لآلة المتجهات الداعمة أحادية الفئة
المجال	تعلم الآلة	تعلم الآلة
العائلة	Machine learning	Machine learning
سنة النشأة	2014–2018	2000s
صاحب الطريقة	Multiple (Guo et al.; Pimentel et al.)	Schölkopf et al. (OCSVM); active variant developed in the anomaly-detection literature (2000s–2010s)
النوع	Active learning + unsupervised deep anomaly detection hybrid	Semi-supervised anomaly/novelty detection with iterative labeling
المصدر التأسيسي	Pimentel, M. A. F., Clifton, D. A., Clifton, L., & Tarassenko, L. (2014). A review of novelty detection. Signal Processing, 99, 215–249.	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.
الأسماء البديلة	AL-Autoencoder anomaly detection, active autoencoder anomaly detection, query-guided autoencoder anomaly detection, active deep anomaly detection	AL-OCSVM, active one-class SVM, active novelty detection SVM, query-driven OCSVM
ذات صلة	6	4
الملخص	Active Learning Autoencoder Anomaly Detection combines an autoencoder's unsupervised reconstruction-error scoring with an active learning query loop. The model flags high-error instances as candidate anomalies, selectively asks a human oracle to label the most informative ones, and iteratively retrains — achieving strong anomaly detection with only a small labeling budget.	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.