Comparar métodos

	Gradient Boosting Semi-supervisado	Aprendizaje semisupervisado
Campo	Aprendizaje automático	Aprendizaje automático
Familia	Machine learning	Machine learning
Año de origen	2006–2010s	1970s–2006 (formalized)
Autor original	Chapelle, Scholkopf & Zien (eds.); applied to GBM variants in subsequent literature	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Tipo	Semi-supervised ensemble (self-training + gradient boosted trees)	Learning paradigm
Fuente seminal	Yarowsky, D. (1995). Unsupervised word sense disambiguation rivaling supervised methods. Proceedings of ACL 1995, 189–196. (Foundational self-training framework underlying pseudo-label approaches.)	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
Alias	pseudo-label gradient boosting, self-training GBM, semi-supervised GBT, label-propagation boosting	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
Relacionados	6	5
Resumen	Semi-supervised gradient boosting combines gradient boosted trees with self-training or pseudo-labeling to exploit large pools of unlabeled data alongside a small labeled set. An initial GBM fit on labeled data assigns confident predictions to unlabeled examples; those pseudo-labeled points are folded back into training and the model is re-boosted, iterating until convergence. This allows practitioners to harness cheap unlabeled data when labels are scarce or expensive.	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.