방법 비교

	앙상블 전이 학습	Voting Ensemble
분야	머신러닝	머신러닝
계열	Machine learning	Machine learning
기원 연도	2010s	1990s–2004
창시자	Various (consolidated in deep learning era, 2010s)	Lam & Suen; Kuncheva, L. I. (systematic treatment)
유형	Ensemble of pre-trained / fine-tuned models	Ensemble (combination of multiple classifiers by vote)
원전	Ganaie, M. A., Hu, M., Malik, A. K., Tanveer, M., & Suganthan, P. N. (2022). Ensemble deep learning: A review. Engineering Applications of Artificial Intelligence, 115, 105151.	Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience. ISBN: 978-0-471-21078-8
별칭	transfer ensemble, multi-model transfer learning, ensemble of fine-tuned models, ETL	majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble
관련	6	5
요약	Ensemble Transfer Learning combines multiple models that were each pre-trained on a large source domain and then fine-tuned on a target task. By aggregating the predictions of several independently fine-tuned models, it achieves higher accuracy and robustness than any single transferred model alone, especially when the target dataset is small.	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.