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Semi-supervised Learning

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.

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Semi-supervised Learning (Combined Labeled and Unlabeled Data Training)

Taksonomisk metoderegister · ml-model / machine-learning

Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. · ISBN 978-0-262-03358-9
Zhu, X. (2005). Semi-supervised learning literature survey. Technical Report 1530, University of Wisconsin-Madison. · URL

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