Compara mètodes

	SVM bayesià d'una classe	Detecció d'anomalies amb autoencoders
Camp	Aprenentatge automàtic	Aprenentatge automàtic
Família	Machine learning	Machine learning
Any d'origen	2001–2010	2006–2014
Autor 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
Tipus	Probabilistic anomaly detection	Unsupervised deep learning (reconstruction-based)
Font seminal	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.
Àlies	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
Relacionats	6	3
Resum	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.