Linganisha mbinu

	Uchunguzi wa Hitilafu kwa Kutumia Kujifunza Amilifu na Kirejeshi Kiotomatiki	Jifunze kwa Vitendo One-class SVM
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	2014–2018	2000s
Mwanzilishi	Multiple (Guo et al.; Pimentel et al.)	Schölkopf et al. (OCSVM); active variant developed in the anomaly-detection literature (2000s–2010s)
Aina	Active learning + unsupervised deep anomaly detection hybrid	Semi-supervised anomaly/novelty detection with iterative labeling
Chanzo asilia	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.
Majina mbadala	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
Zinazohusiana	6	4
Muhtasari	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.