विधियों की तुलना करें

	वन-क्लास एसवीएम	वैरिएशन ऑटोएन्कोडर
क्षेत्र	मशीन अधिगम	गहन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	1999–2001	2014
प्रवर्तक	Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C.	Kingma, D. P. & Welling, M.
प्रकार	Anomaly / novelty detection (unsupervised)	Deep generative latent-variable model (encoder–decoder)
मौलिक स्रोत	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).
उपनाम	OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM	Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model
संबंधित	3	5
सारांश	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.