Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| SVM pentru o singură clasă× | Isolation Forest× | |
|---|---|---|
| Domeniu | Învățare automată | Învățare automată |
| Familie | Machine learning | Machine learning |
| Anul apariției≠ | 1999–2001 | 2008 |
| Autorul original≠ | Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C. | Liu, F.T., Ting, K.M. & Zhou, Z.-H. |
| Tip≠ | Anomaly / novelty detection (unsupervised) | Unsupervised ensemble (random partitioning trees) |
| Sursa seminală≠ | Scholkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001). Estimating the support of a high-dimensional distribution. Neural Computation, 13(7), 1443–1471. DOI ↗ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422. DOI ↗ |
| Denumiri alternative≠ | OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM | Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection |
| Înrudite≠ | 3 | 5 |
| Rezumat≠ | One-class SVM is an unsupervised anomaly and novelty detection algorithm that learns a tight boundary around normal training data in a kernel-induced feature space, flagging new observations that fall outside that boundary as outliers. Introduced by Scholkopf et al. in 1999–2001, it extends the SVM framework to the single-class setting where no labelled anomalies are available. | 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. |
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