পদ্ধতির তুলনা করুন

	দৃঢ় ওয়ান-ক্লাস এসভিএম	আইসোলেশন ফরেস্ট
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2000s–2010s	2008
প্রবর্তক	Extensions of Scholkopf et al. (1999); robust variants developed in 2000s–2010s	Liu, F.T., Ting, K.M. & Zhou, Z.-H.
ধরন	Anomaly detection / novelty detection	Unsupervised ensemble (random partitioning trees)
মৌলিক উৎস	Scholkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., & Platt, J. (1999). Support vector method for novelty detection. Advances in Neural Information Processing Systems (NeurIPS), 12, 582–588.	Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422.
অপর নাম	Robust OCSVM, Outlier-robust One-Class SVM, Contamination-tolerant OCSVM, Robust novelty detection SVM	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection
সম্পর্কিত	5	5
সারসংক্ষেপ	Robust One-Class SVM extends the classic One-Class Support Vector Machine for novelty and anomaly detection by incorporating robustness mechanisms — such as trimmed objectives, robust kernel choices, or contamination-tolerant loss functions — that reduce the influence of heavy-tailed noise or outliers present in the training data, yielding a decision boundary that better represents the true support of the normal class.	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.