Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Vienas klases SVM× | Autoencoder anomāliju noteikšana× | |
|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 1999–2001 | 2006–2014 |
| Autors≠ | Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C. | Hinton, G. E. & Salakhutdinov, R. R. (autoencoders); applied to anomaly detection through multiple authors in the 2010s |
| Tips≠ | Anomaly / novelty detection (unsupervised) | Unsupervised deep learning (reconstruction-based) |
| Pirmavots≠ | 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 ↗ | Chalapathy, R. & Chawla, S. (2019). Deep learning for anomaly detection: A survey. arXiv preprint arXiv:1901.03407. link ↗ |
| Citi nosaukumi | OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM | AE anomaly detection, reconstruction-error anomaly detection, deep autoencoder outlier detection, unsupervised autoencoder anomaly detection |
| Saistītās | 3 | 3 |
| Kopsavilkums≠ | 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. | Autoencoder anomaly detection trains a neural network to compress and then reconstruct normal data. Because the model has only ever learned what normal looks like, anomalous inputs produce noticeably higher reconstruction errors — and those errors become the anomaly score. The method requires no labeled anomalies and scales naturally to high-dimensional data such as sensor streams, images, and log records. |
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