Võrdle meetodeid

	Isolation Forest	Üheklassi SVM	Variational Autoencoder
Valdkond	Masinõpe	Masinõpe	Süvaõpe
Perekond	Machine learning	Machine learning	Machine learning
Tekkeaasta	2008	1999–2001	2014
Looja	Liu, F.T., Ting, K.M. & Zhou, Z.-H.	Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C.	Kingma, D. P. & Welling, M.
Tüüp	Unsupervised ensemble (random partitioning trees)	Anomaly / novelty detection (unsupervised)	Deep generative latent-variable model (encoder–decoder)
Algallikas	Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422.	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.	Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR).
Rööpnimetused	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection	OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM	Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model
Seotud	5	3	5
Kokkuvõte	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.	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.	The Variational Autoencoder (VAE) is a deep generative latent-variable model, introduced by Diederik Kingma and Max Welling in 2014, that encodes data as a probability distribution in a latent space and samples from that distribution to generate new examples. It is used for data generation, anomaly detection, and feature learning.