Σύγκριση μεθόδων

	Semi-supervised Metric Learning	Ημι-επιβλεπόμενη Μάθηση
Πεδίο	Μηχανική Μάθηση	Μηχανική Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2007–2008	1970s–2006 (formalized)
Δημιουργός	Yeung, D.-Y. & Chang, H.; Davis, J. V. & Dhillon, I. S.	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Τύπος	Hybrid supervised/unsupervised distance learning	Learning paradigm
Θεμελιώδης πηγή	Yeung, D.-Y., & Chang, H. (2007). A kernel approach for semi-supervised metric learning. IEEE Transactions on Neural Networks, 18(1), 141–149.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
Εναλλακτικές ονομασίες	SSML, semi-supervised distance learning, constrained metric learning, weakly supervised metric learning	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
Συναφείς	5	5
Σύνοψη	Semi-supervised metric learning learns a task-adapted distance function by combining a small set of labeled pairwise constraints — must-link and cannot-link pairs — with the geometric structure of a much larger pool of unlabeled data. The result is a Mahalanobis-style or kernel-based distance that reflects both supervision and data topology, improving downstream tasks such as nearest-neighbor classification and clustering.	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.