Methoden vergleichen

	Regularisierte Online-Lernverfahren	Transfer Learning
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	2007–2013	2010 (formalized); 1990s (early roots)
Urheber	Xiao, L.; Shalev-Shwartz, S.; McMahan, H. B. et al.	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
Typ	Online optimization framework with regularization	Learning paradigm
Wegweisende Quelle	Xiao, L. (2010). Dual Averaging Methods for Regularized Stochastic and Online Optimization. Journal of Machine Learning Research, 11, 2543–2596.	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
Aliasnamen	FTRL, Follow-the-Regularized-Leader, online regularized optimization, regularized dual averaging	TL, domain adaptation, fine-tuning, pre-trained model adaptation
Verwandt	6	3
Zusammenfassung	Regularized online learning extends the online learning paradigm by incorporating a regularization penalty into each weight update, controlling model complexity while processing data one example at a time. Algorithms such as Follow-the-Regularized-Leader (FTRL) and Regularized Dual Averaging (RDA) make this approach practical at scale, enabling sparse, well-calibrated models on streaming data.	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.