방법 비교

	온라인 원클래스 SVM	One-Class SVM
분야	머신러닝	머신러닝
계열	Machine learning	Machine learning
기원 연도	2006 (incremental/online variant); 1999 (base method)	1999–2001
창시자	Laskov, P. et al. (incremental extension); Scholkopf, B. et al. (original OC-SVM)	Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C.
유형	Online anomaly detection / novelty detection	Anomaly / novelty detection (unsupervised)
원전	Laskov, P., Gehl, C., Krueger, S., & Muller, K.-R. (2006). Incremental support vector learning: Analysis, implementation and applications. Journal of Machine Learning Research, 7, 1909–1936.	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.
별칭	Online OC-SVM, Incremental One-Class SVM, Online SVDD, Sequential One-Class SVM	OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM
관련	4	3
요약	Online One-Class SVM is an incremental extension of the classical One-Class Support Vector Machine that updates its decision boundary as new data arrive one sample at a time, making it suitable for streaming environments and real-time anomaly or novelty detection without retraining from scratch.	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.