विधियों की तुलना करें

	अर्ध-पर्यवेक्षित ऑटोएन्कोडर विसंगति का पता लगाना	वन-क्लास एसवीएम
क्षेत्र	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2018–2020	1999–2001
प्रवर्तक	Ruff, L. et al.; Zong, B. et al.	Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C.
प्रकार	Semi-supervised deep anomaly detection	Anomaly / novelty detection (unsupervised)
मौलिक स्रोत	Ruff, L., Vandermeulen, R. A., Franks, B. J., Müller, K.-R., & Kloft, M. (2020). Deep Semi-Supervised Anomaly Detection. In International Conference on Learning Representations (ICLR 2020).	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.
उपनाम	Semi-supervised AE anomaly detection, SSAD autoencoder, semi-supervised reconstruction-error detection, partially labeled autoencoder anomaly detection	OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM
संबंधित	5	3
सारांश	Semi-supervised Autoencoder Anomaly Detection trains a neural autoencoder primarily on normal (unlabeled) data, then uses a small set of labeled anomalies to refine decision boundaries, detecting anomalies as samples with high reconstruction error. It bridges the gap between purely unsupervised autoencoders and fully supervised classifiers when labels are scarce but some known anomalies exist.	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.