Comparar métodos

	Aprendizagem de Poucas Amostras por Ensemble	Boosting
Área	Aprendizado de máquina	Aprendizado de máquina
Família	Machine learning	Machine learning
Ano de origem	2019	1990–1997
Autor original	Dvornik, N., Schmid, C., & Mairal, J.	Schapire, R. E.; Freund, Y.
Tipo	Ensemble of few-shot learners	Sequential ensemble (iterative reweighting)
Fonte seminal	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.	Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139.
Outros nomes	ensemble few-shot classification, multi-model few-shot learning, few-shot ensemble, cooperative few-shot ensemble	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble
Relacionados	5	6
Resumo	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.	Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy.