Linganisha mbinu

	Jifunze kwa Vitendo One-class SVM	Kujifunza kwa Njia Amilifu
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	2000s	2009
Mwanzilishi	Schölkopf et al. (OCSVM); active variant developed in the anomaly-detection literature (2000s–2010s)	Burr Settles
Aina	Semi-supervised anomaly/novelty detection with iterative labeling	Interactive supervised learning framework
Chanzo asilia	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.	Settles, B. (2009). Active learning literature survey. University of Wisconsin-Madison Computer Sciences Technical Report 1648.
Majina mbadala	AL-OCSVM, active one-class SVM, active novelty detection SVM, query-driven OCSVM	Query Learning, Optimal Experimental Design (ML context), Pool-Based Active Learning, Aktif Öğrenme
Zinazohusiana	4	2
Muhtasari	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.	Active learning is an iterative machine-learning paradigm in which a learning algorithm selectively queries an oracle — typically a human annotator — for labels on the most informative unlabeled examples. Formalized by Burr Settles in his seminal 2009 literature survey, active learning addresses the practical bottleneck of annotation cost by achieving high model accuracy with far fewer labeled examples than passive supervised learning requires.