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	Polunasledni Naivni Bejz	Semi-supervised Learning
Oblast	Mašinsko učenje	Mašinsko učenje
Porodica	Machine learning	Machine learning
Godina nastanka	2000	1970s–2006 (formalized)
Tvorac	Nigam, K.; McCallum, A. K.; Thrun, S.; Mitchell, T.	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Tip	Semi-supervised generative classifier	Learning paradigm
Temeljni izvor	Nigam, K., McCallum, A. K., Thrun, S., & Mitchell, T. (2000). Text Classification from Labeled and Unlabeled Documents using EM. Machine Learning, 39(2–3), 103–134.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
Drugi nazivi	SSL Naive Bayes, EM-Naive Bayes, semi-supervised generative classifier, Nigam et al. text classifier	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
Srodne	4	5
Sažetak	Semi-supervised Naive Bayes extends the classic Naive Bayes generative model to exploit large pools of unlabeled data alongside a small labeled set. Using Expectation-Maximization, it iteratively infers soft class assignments for unlabeled examples and re-estimates class and feature parameters, yielding substantially better classifiers when labeled examples are scarce.	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.