Methoden vergelijken

	One-Class SVM	Autoencoder Anomaliedetectie
Vakgebied	Machine learning	Machine learning
Familie	Machine learning	Machine learning
Jaar van ontstaan	1999–2001	2006–2014
Grondlegger	Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C.	Hinton, G. E. & Salakhutdinov, R. R. (autoencoders); applied to anomaly detection through multiple authors in the 2010s
Type	Anomaly / novelty detection (unsupervised)	Unsupervised deep learning (reconstruction-based)
Oorspronkelijke bron	Scholkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001). Estimating the support of a high-dimensional distribution. Neural Computation, 13(7), 1443–1471.	Chalapathy, R. & Chawla, S. (2019). Deep learning for anomaly detection: A survey. arXiv preprint arXiv:1901.03407.
Aliassen	OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM	AE anomaly detection, reconstruction-error anomaly detection, deep autoencoder outlier detection, unsupervised autoencoder anomaly detection
Verwant	3	3
Samenvatting	One-class SVM is an unsupervised anomaly and novelty detection algorithm that learns a tight boundary around normal training data in a kernel-induced feature space, flagging new observations that fall outside that boundary as outliers. Introduced by Scholkopf et al. in 1999–2001, it extends the SVM framework to the single-class setting where no labelled anomalies are available.	Autoencoder anomaly detection trains a neural network to compress and then reconstruct normal data. Because the model has only ever learned what normal looks like, anomalous inputs produce noticeably higher reconstruction errors — and those errors become the anomaly score. The method requires no labeled anomalies and scales naturally to high-dimensional data such as sensor streams, images, and log records.