Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Robust One-Class SVM× | Ізоляційний ліс× | |
|---|---|---|
| Галузь | Машинне навчання | Машинне навчання |
| Родина | Machine learning | Machine learning |
| Рік появи≠ | 2000s–2010s | 2008 |
| Автор методу≠ | Extensions of Scholkopf et al. (1999); robust variants developed in 2000s–2010s | Liu, F.T., Ting, K.M. & Zhou, Z.-H. |
| Тип≠ | Anomaly detection / novelty detection | Unsupervised ensemble (random partitioning trees) |
| Основоположне джерело≠ | 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. link ↗ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422. DOI ↗ |
| Інші назви≠ | Robust OCSVM, Outlier-robust One-Class SVM, Contamination-tolerant OCSVM, Robust novelty detection SVM | Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection |
| Пов'язані | 5 | 5 |
| Підсумок≠ | 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. |
| ScholarGateНабір даних ↗ |
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