পদ্ধতির তুলনা করুন

	অ্যাক্টিভ লার্নিং ওয়ান-ক্লাস এসভিএম	আইসোলেশন ফরেস্ট
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2000s	2008
প্রবর্তক	Schölkopf et al. (OCSVM); active variant developed in the anomaly-detection literature (2000s–2010s)	Liu, F.T., Ting, K.M. & Zhou, Z.-H.
ধরন	Semi-supervised anomaly/novelty detection with iterative labeling	Unsupervised ensemble (random partitioning trees)
মৌলিক উৎস	Schölkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (1999). Estimating the Support of a High-Dimensional Distribution. Neural Computation, 13(7), 1443–1471.	Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422.
অপর নাম	AL-OCSVM, active one-class SVM, active novelty detection SVM, query-driven OCSVM	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection
সম্পর্কিত	4	5
সারসংক্ষেপ	Active Learning One-class SVM combines the one-class support vector machine — a kernel-based novelty detector that learns the boundary of normal data — with an active learning loop that selects the most informative unlabeled instances for expert annotation. The result is a data-efficient anomaly detector that improves its decision boundary with minimal labeling effort.	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.