Comparar métodos

	Detecção de Anomalias com Autoencoder Semi-supervisionado	SVM de Uma Classe Semissupervisionado
Área	Aprendizado de máquina	Aprendizado de máquina
Família	Machine learning	Machine learning
Ano de origem	2018–2020	2001–2004
Autor original	Ruff, L. et al.; Zong, B. et al.	Extension of Scholkopf et al. (2001); semi-supervised variants studied ca. 2004–2010
Tipo	Semi-supervised deep anomaly detection	Semi-supervised anomaly / novelty detection
Fonte seminal	Ruff, L., Vandermeulen, R. A., Franks, B. J., Müller, K.-R., & Kloft, M. (2020). Deep Semi-Supervised Anomaly Detection. In International Conference on Learning Representations (ICLR 2020).	Munoz, A. & Muruzabal, J. (2004). Self-Organising Maps for Outlier Detection. Neurocomputing, 58–60, 953–956.
Outros nomes	Semi-supervised AE anomaly detection, SSAD autoencoder, semi-supervised reconstruction-error detection, partially labeled autoencoder anomaly detection	SS-OCSVM, semi-supervised OC-SVM, semi-supervised novelty detection SVM, transductive one-class SVM
Relacionados	5	5
Resumo	Semi-supervised Autoencoder Anomaly Detection trains a neural autoencoder primarily on normal (unlabeled) data, then uses a small set of labeled anomalies to refine decision boundaries, detecting anomalies as samples with high reconstruction error. It bridges the gap between purely unsupervised autoencoders and fully supervised classifiers when labels are scarce but some known anomalies exist.	Semi-supervised One-class SVM extends the classic One-class SVM anomaly detector by incorporating unlabeled observations alongside a small set of known normal examples. The unlabeled data helps the model learn a tighter, more informative decision boundary in feature space, reducing false positives and improving anomaly recall compared to the purely unsupervised baseline.