Krahasoni metodat
Shqyrtoni metodat e zgjedhura krah për krah; rreshtat që ndryshojnë janë të theksuar.
| SVM një-klasë robust× | Pylli i Izolimit i Qëndrueshëm (Robust Isolation Forest)× | |
|---|---|---|
| Fusha | Mësimi i makinës | Mësimi i makinës |
| Familja | Machine learning | Machine learning |
| Viti i origjinës≠ | 2000s–2010s | 2008–2019 |
| Krijuesi≠ | Extensions of Scholkopf et al. (1999); robust variants developed in 2000s–2010s | Liu, F. T., Ting, K. M., Zhou, Z.-H. (base); robust extensions by multiple authors |
| Lloji≠ | Anomaly detection / novelty detection | Robust ensemble anomaly detection |
| Burimi themelues≠ | Scholkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., & Platt, J. (1999). Support vector method for novelty detection. Advances in Neural Information Processing Systems (NeurIPS), 12, 582–588. link ↗ | Liu, F. T., Ting, K. M., & Zhou, Z.-H. (2008). Isolation Forest. In Proceedings of the IEEE International Conference on Data Mining (ICDM), pp. 413–422. IEEE. DOI ↗ |
| Emërtime të tjera | Robust OCSVM, Outlier-robust One-Class SVM, Contamination-tolerant OCSVM, Robust novelty detection SVM | Robust iForest, noise-robust isolation forest, contamination-robust isolation forest, robust anomaly isolation |
| Të lidhura | 5 | 5 |
| Përmbledhja≠ | Robust One-Class SVM extends the classic One-Class Support Vector Machine for novelty and anomaly detection by incorporating robustness mechanisms — such as trimmed objectives, robust kernel choices, or contamination-tolerant loss functions — that reduce the influence of heavy-tailed noise or outliers present in the training data, yielding a decision boundary that better represents the true support of the normal class. | Robust Isolation Forest extends the classic Isolation Forest anomaly detector with strategies that reduce sensitivity to data contamination, masking effects, and biased random splits. By incorporating robustness mechanisms — such as improved subsampling, re-weighting of suspicious regions, or bias-corrected splitting — it achieves more reliable anomaly scores when the training data itself contains a non-trivial fraction of anomalies or when specific feature distributions cause standard iForest to produce unreliable path lengths. |
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