Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Tiešsaistes vienas klases SVM× | Vienas klases SVM× | |
|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2006 (incremental/online variant); 1999 (base method) | 1999–2001 |
| Autors≠ | Laskov, P. et al. (incremental extension); Scholkopf, B. et al. (original OC-SVM) | Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C. |
| Tips≠ | Online anomaly detection / novelty detection | Anomaly / novelty detection (unsupervised) |
| Pirmavots≠ | 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 ↗ | 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 ↗ |
| Citi nosaukumi | Online OC-SVM, Incremental One-Class SVM, Online SVDD, Sequential One-Class SVM | OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM |
| Saistītās≠ | 4 | 3 |
| Kopsavilkums≠ | 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. | 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. |
| ScholarGateDatu kopa ↗ |
|
|