So sánh phương pháp

	Active Learning One-class SVM	Học bán giám sát
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2000s	1970s–2006 (formalized)
Người khởi xướng	Schölkopf et al. (OCSVM); active variant developed in the anomaly-detection literature (2000s–2010s)	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Loại	Semi-supervised anomaly/novelty detection with iterative labeling	Learning paradigm
Công trình gốc	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.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
Tên gọi khác	AL-OCSVM, active one-class SVM, active novelty detection SVM, query-driven OCSVM	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
Liên quan	4	5
Tóm tắt	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.	Semi-supervised learning (SSL) is a machine learning paradigm that trains models using a small set of labeled examples together with a much larger pool of unlabeled data. By leveraging the structure inherent in unlabeled data, SSL achieves accuracy closer to fully supervised models while requiring far fewer costly manual labels — making it practical when labeling is expensive, slow, or resource-constrained.