Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Самообучающаяся One-class SVM× | Гауссовский процесс× | |
|---|---|---|
| Область | Машинное обучение | Машинное обучение |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 2018 | 2006 (book); roots in Kriging, 1951) |
| Автор метода≠ | Golan & El-Yaniv; Ruff et al. | Rasmussen, C. E. & Williams, C. K. I. |
| Тип≠ | Self-supervised anomaly/novelty detection | Probabilistic non-parametric model |
| Основополагающий источник≠ | Golan, I. & El-Yaniv, R. (2018). Deep One-Class Classification. Proceedings of the 35th International Conference on Machine Learning (ICML), PMLR 80, 1747–1756. link ↗ | Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9 |
| Другие названия | SS-OCSVM, Self-supervised SVDD, Self-supervised novelty detection, Pretext-task OC-SVM | GP, Gaussian Process Regression, GPR, Kriging |
| Связанные≠ | 6 | 3 |
| Сводка≠ | Self-supervised One-class SVM combines pretext-task-based representation learning with One-class SVM to detect anomalies and novelties without requiring labeled anomaly examples. The model first learns expressive feature embeddings from normal data alone, then fits an OC-SVM boundary in the learned feature space to flag out-of-distribution samples. | A Gaussian Process (GP) is a non-parametric, fully probabilistic machine learning model that places a prior distribution directly over functions. Rather than predicting a single value, it returns a predictive mean and a calibrated uncertainty estimate at every test point, making it especially valuable for regression on small to medium datasets and for Bayesian optimization tasks. |
| ScholarGateНабор данных ↗ |
|
|