Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Online One-Class SVM× | Isolation Forest× | |
|---|---|---|
| Domeniu | Învățare automată | Învățare automată |
| Familie | Machine learning | Machine learning |
| Anul apariției≠ | 2006 (incremental/online variant); 1999 (base method) | 2008 |
| Autorul original≠ | Laskov, P. et al. (incremental extension); Scholkopf, B. et al. (original OC-SVM) | Liu, F.T., Ting, K.M. & Zhou, Z.-H. |
| Tip≠ | Online anomaly detection / novelty detection | Unsupervised ensemble (random partitioning trees) |
| Sursa seminală≠ | 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. link ↗ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422. DOI ↗ |
| Denumiri alternative≠ | Online OC-SVM, Incremental One-Class SVM, Online SVDD, Sequential One-Class SVM | Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection |
| Înrudite≠ | 4 | 5 |
| Rezumat≠ | 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. |
| ScholarGateSet de date ↗ |
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