Sammenlign metoder

	Autoencoder	Isolation Forest	One-Class SVM
Fagområde	Dyb læring	Maskinlæring	Maskinlæring
Familie	Machine learning	Machine learning	Machine learning
Oprindelsesår	2006	2008	1999–2001
Ophavsperson	Hinton, G.E. & Salakhutdinov, R.R.	Liu, F.T., Ting, K.M. & Zhou, Z.-H.	Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C.
Type	Neural network (encoder-decoder)	Unsupervised ensemble (random partitioning trees)	Anomaly / novelty detection (unsupervised)
Oprindelig kilde	Hinton, G.E. & Salakhutdinov, R.R. (2006). Reducing the Dimensionality of Data with Neural Networks. Science, 313(5786), 504–507.	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.
Aliasser	Otokodlayıcı (Autoencoder), otokodlayıcı, auto-encoder, encoder-decoder network	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection	OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM
Relaterede	4	5	3
Resumé	An autoencoder is an encoder-decoder neural network, popularised by Hinton and Salakhutdinov in 2006, that compresses data into a low-dimensional latent code and then reconstructs it, enabling dimensionality reduction and anomaly detection. By learning to rebuild its own input through a narrow bottleneck, it discovers a compact representation of the data.	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.