Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| One-class SVM× | Локальний коефіцієнт викидів (LOF)× | |
|---|---|---|
| Галузь | Машинне навчання | Машинне навчання |
| Родина | Machine learning | Machine learning |
| Рік появи≠ | 1999–2001 | 2000 |
| Автор методу≠ | Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C. | Breunig, M. M.; Kriegel, H.-P.; Ng, R. T.; Sander, J. |
| Тип≠ | Anomaly / novelty detection (unsupervised) | Density-based anomaly detection (unsupervised) |
| Основоположне джерело≠ | 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 ↗ | Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000). LOF: Identifying density-based local outliers. Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, 93–104. DOI ↗ |
| Інші назви | OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM | LOF, local outlier factor, density-based outlier detection, local density deviation |
| Пов'язані≠ | 3 | 4 |
| Підсумок≠ | 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. | Local Outlier Factor (LOF) is a density-based, unsupervised anomaly detection algorithm introduced by Breunig, Kriegel, Ng, and Sander in 2000. It assigns each data point a continuous outlier score that quantifies how isolated that point is relative to its local neighborhood, enabling detection of anomalies that global methods miss because they blend into dense clusters elsewhere in the space. |
| ScholarGateНабір даних ↗ |
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