Methoden vergleichen

	Bayesian Semi-supervised Learning	Transfer Learning
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	2003–2006	2010 (formalized); 1990s (early roots)
Urheber	Chapelle, Scholkopf & Zien; Zhu, Ghahramani & Lafferty	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
Typ	Probabilistic semi-supervised framework	Learning paradigm
Wegweisende Quelle	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.). (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
Aliasnamen	Bayesian SSL, probabilistic semi-supervised learning, generative semi-supervised model, Bayesian transductive learning	TL, domain adaptation, fine-tuning, pre-trained model adaptation
Verwandt	6	3
Zusammenfassung	Bayesian semi-supervised learning is a probabilistic framework that uses both a small labeled dataset and a larger pool of unlabeled observations to infer model parameters and make predictions. By treating missing labels as latent variables and placing priors over parameters, it naturally quantifies uncertainty while leveraging unlabeled data to improve generalization.	Transfer learning is a machine learning paradigm in which knowledge gained from training a model on a source task or domain is reused to improve learning on a different but related target task or domain. It is especially powerful when labeled data for the target task is scarce, and it underlies most modern deep learning applications in computer vision, natural language processing, and beyond.