Methoden vergleichen

	Semi-überwachte logistische Regression	Semi-Supervised Learning
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	1995–2000	1970s–2006 (formalized)
Urheber	Nigam, K.; McCallum, A. et al. (EM variant); Yarowsky, D. (self-training)	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Typ	Semi-supervised classifier	Learning paradigm
Wegweisende Quelle	Nigam, K., McCallum, A., Thrun, S., & Mitchell, T. (2000). Text classification from labeled and unlabeled documents using EM. Machine Learning, 39, 103–134.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
Aliasnamen	SSL logistic regression, semi-supervised LR, EM logistic regression, self-training logistic classifier	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
Verwandt	5	5
Zusammenfassung	Semi-supervised logistic regression extends the standard logistic classifier by incorporating unlabeled data during training. Using self-training, expectation-maximization, or label-propagation wrappers, it iteratively assigns soft labels to unlabeled examples and refines model parameters, improving generalization when labeled data are scarce relative to the full dataset.	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.