পদ্ধতির তুলনা করুন

	অনলাইন ওয়ান-ক্লাস SVM	আইসোলেশন ফরেস্ট
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2006 (incremental/online variant); 1999 (base method)	2008
প্রবর্তক	Laskov, P. et al. (incremental extension); Scholkopf, B. et al. (original OC-SVM)	Liu, F.T., Ting, K.M. & Zhou, Z.-H.
ধরন	Online anomaly detection / novelty detection	Unsupervised ensemble (random partitioning trees)
মৌলিক উৎস	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.	Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422.
অপর নাম	Online OC-SVM, Incremental One-Class SVM, Online SVDD, Sequential One-Class SVM	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection
সম্পর্কিত	4	5
সারসংক্ষেপ	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.	Isolation Forest is an unsupervised machine-learning method for anomaly and outlier detection, introduced by Liu, Ting and Zhou in 2008, that isolates anomalies through random partitioning of the data. It works without any labelled anomaly data and scales to high-dimensional datasets.