Compara mètodes

	SVM d'una sola classe	Detecció d'anomalies amb autoencoders	Isolation Forest
Camp	Aprenentatge automàtic	Aprenentatge automàtic	Aprenentatge automàtic
Família	Machine learning	Machine learning	Machine learning
Any d'origen	1999–2001	2006–2014	2008
Autor original	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	Liu, F.T., Ting, K.M. & Zhou, Z.-H.
Tipus	Anomaly / novelty detection (unsupervised)	Unsupervised deep learning (reconstruction-based)	Unsupervised ensemble (random partitioning trees)
Font seminal	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.	Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422.
Àlies	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	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection
Relacionats	3	3	5
Resum	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.	Isolation Forest is an unsupervised machine-learning method for anomaly and outlier detection, introduced by Liu, Ting and Zhou in 2008, that isolates anomalies through random partitioning of the data. It works without any labelled anomaly data and scales to high-dimensional datasets.