Methoden vergleichen

	Metrik-Lernen	Semi-Supervised Learning
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	2003 (foundational); refined 2009 (LMNN)	1970s–2006 (formalized)
Urheber	Xing, E. P.; Jordan, M. I.; Russell, S.; Ng, A. Y.	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Typ	Representation learning / supervised distance optimization	Learning paradigm
Wegweisende Quelle	Xing, E. P., Jordan, M. I., Russell, S., & Ng, A. Y. (2003). Distance metric learning with application to clustering with side-information. In Advances in Neural Information Processing Systems (NIPS), 16, 505–512.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
Aliasnamen	Distance Metric Learning, Similarity Learning, DML, Representation Learning via Distance	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
Verwandt	5	5
Zusammenfassung	Metric learning is a machine-learning framework that trains a distance or similarity function from data so that semantically similar examples end up close together in the learned space while dissimilar examples are pushed apart. Unlike fixed distances such as Euclidean, the learned metric adapts to the structure of the task, making downstream classifiers, clusterers, and retrieval systems significantly more accurate.	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.