Uporedite metode

	Polu-nadgledani jednoklasni SVM	Semi-supervised Learning
Oblast	Mašinsko učenje	Mašinsko učenje
Porodica	Machine learning	Machine learning
Godina nastanka	2001–2004	1970s–2006 (formalized)
Tvorac	Extension of Scholkopf et al. (2001); semi-supervised variants studied ca. 2004–2010	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Tip	Semi-supervised anomaly / novelty detection	Learning paradigm
Temeljni izvor	Munoz, A. & Muruzabal, J. (2004). Self-Organising Maps for Outlier Detection. Neurocomputing, 58–60, 953–956.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
Drugi nazivi	SS-OCSVM, semi-supervised OC-SVM, semi-supervised novelty detection SVM, transductive one-class SVM	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
Srodne	5	5
Sažetak	Semi-supervised One-class SVM extends the classic One-class SVM anomaly detector by incorporating unlabeled observations alongside a small set of known normal examples. The unlabeled data helps the model learn a tighter, more informative decision boundary in feature space, reducing false positives and improving anomaly recall compared to the purely unsupervised baseline.	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.