Bandingkan metode

	Deteksi Anomali Autoencoder Daring	SVM Satu Kelas
Bidang	Pembelajaran Mesin	Pembelajaran Mesin
Keluarga	Machine learning	Machine learning
Tahun asal	2010s–present	1999–2001
Pencetus	Various (online/incremental deep learning community)	Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C.
Tipe	Online unsupervised anomaly detection	Anomaly / novelty detection (unsupervised)
Sumber perintis	An, J. & Cho, S. (2015). Variational Autoencoder based Anomaly Detection using Reconstruction Probability. SNU Data Mining Center, 2015-2.	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.
Alias	incremental autoencoder anomaly detection, streaming autoencoder anomaly detection, online AE anomaly detection, continual autoencoder anomaly detection	OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM
Terkait	5	3
Ringkasan	Online Autoencoder Anomaly Detection trains an autoencoder incrementally on a continuous data stream, flagging observations whose reconstruction error exceeds an adaptive threshold as anomalies. This approach combines the representational power of deep autoencoders with the incremental update capability of online learning, making it suitable for real-time or high-volume streaming scenarios where batch retraining is impractical.	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.