方法对比

	集成少样本学习	投票集成 (Voting Ensemble)
领域	机器学习	机器学习
方法族	Machine learning	Machine learning
起源年份	2019	1990s–2004
提出者	Dvornik, N., Schmid, C., & Mairal, J.	Lam & Suen; Kuncheva, L. I. (systematic treatment)
类型	Ensemble of few-shot learners	Ensemble (combination of multiple classifiers by vote)
开创性文献	Dvornik, N., Schmid, C., & Mairal, J. (2019). Diversity with Cooperation: Ensemble Methods for Few-Shot Classification. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 3716–3725.	Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience. ISBN: 978-0-471-21078-8
别名	ensemble few-shot classification, multi-model few-shot learning, few-shot ensemble, cooperative few-shot ensemble	majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble
相关	5	5
摘要	Ensemble Few-Shot Learning combines multiple few-shot models — such as prototypical networks or embedding learners — to classify new classes from only one to a handful of labeled examples. By enforcing diversity among base learners and aggregating their predictions, the ensemble consistently outperforms any single few-shot model in accuracy and robustness, especially under severe label scarcity.	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.