Σύγκριση μεθόδων

	Ημι-επιβλεπόμενη Μάθηση	Σύνολο Ψηφοφορίας (Voting Ensemble)
Πεδίο	Μηχανική Μάθηση	Μηχανική Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	1970s–2006 (formalized)	1990s–2004
Δημιουργός	Vapnik, V. N. and others (community of researchers, 1970s–2000s)	Lam & Suen; Kuncheva, L. I. (systematic treatment)
Τύπος	Learning paradigm	Ensemble (combination of multiple classifiers by vote)
Θεμελιώδης πηγή	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9	Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience. ISBN: 978-0-471-21078-8
Εναλλακτικές ονομασίες	SSL, semi-supervised machine learning, transductive learning, label-efficient learning	majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble
Συναφείς	5	5
Σύνοψη	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.	A voting ensemble trains several diverse classifiers independently and combines their predictions by a vote: hard voting picks the class chosen by the most models, while soft voting averages their class-probability estimates, optionally with per-model weights. The combination usually outperforms any individual member, and requires no additional training after the base models are fitted.