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	Självövervakad Support Vector Machine	Semi-övervakad inlärning
Ämnesområde	Maskininlärning	Maskininlärning
Familj	Machine learning	Machine learning
Ursprungsår	2019–2021	1970s–2006 (formalized)
Upphovsperson	Various (integration of self-supervised learning with SVM classifiers, ~2019–2021)	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Typ	Hybrid (self-supervised pretraining + SVM classifier)	Learning paradigm
Ursprungskälla	De Palma, A., Bucarelli, M. S., Goyal, P., & Silvestri, F. (2021). Self-supervised Support Vector Machine. Proceedings of the AAAI Workshop on Self-Supervised Learning for the Internet of Things.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
Alias	Self-supervised SVM, SS-SVM, semi-self-supervised SVM, self-supervised kernel SVM	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
Närliggande	5	5
Sammanfattning	A Self-supervised Support Vector Machine combines self-supervised pretraining — learning representations from unlabeled data via pretext tasks — with a Support Vector Machine classifier trained on the resulting features. This hybrid approach enables strong classification performance even when labeled data is scarce, by leveraging the structure embedded in large unlabeled datasets before applying the SVM's margin-maximization objective.	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.