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| SVM One-Class Online× | Isolation Forest× | |
|---|---|---|
| Campo | Apprendimento automatico | Apprendimento automatico |
| Famiglia | Machine learning | Machine learning |
| Anno di origine≠ | 2006 (incremental/online variant); 1999 (base method) | 2008 |
| Ideatore≠ | Laskov, P. et al. (incremental extension); Scholkopf, B. et al. (original OC-SVM) | Liu, F.T., Ting, K.M. & Zhou, Z.-H. |
| Tipo≠ | Online anomaly detection / novelty detection | Unsupervised ensemble (random partitioning trees) |
| Fonte seminale≠ | 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 ↗ |
| Alias≠ | Online OC-SVM, Incremental One-Class SVM, Online SVDD, Sequential One-Class SVM | Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection |
| Correlati≠ | 4 | 5 |
| Sintesi≠ | 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. |
| ScholarGateInsieme di dati ↗ |
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