Compară metode

	SVM bayesian de o singură clasă	Detecția anomaliilor cu autoencoder
Domeniu	Învățare automată	Învățare automată
Familie	Machine learning	Machine learning
Anul apariției	2001–2010	2006–2014
Autorul original	Scholkopf et al. (base OCSVM); Bayesian extension via Tipping and others	Hinton, G. E. & Salakhutdinov, R. R. (autoencoders); applied to anomaly detection through multiple authors in the 2010s
Tip	Probabilistic anomaly detection	Unsupervised deep learning (reconstruction-based)
Sursa seminală	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.
Denumiri alternative	Bayesian OCSVM, Bayesian one-class classifier, probabilistic one-class SVM, Bayes-OCSVM	AE anomaly detection, reconstruction-error anomaly detection, deep autoencoder outlier detection, unsupervised autoencoder anomaly detection
Înrudite	6	3
Rezumat	Bayesian one-class SVM combines the classical one-class support vector machine — which learns a tight boundary around normal training examples — with Bayesian inference to produce calibrated probability estimates of anomaly, rather than only a binary flag. This allows uncertainty quantification over the novelty decision, making the approach more suitable when downstream actions depend on how confident the model is that a new observation is anomalous.	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.