Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| SVM ya Daraja Moja inayojifundisha× | Mchakato wa Gaussia× | |
|---|---|---|
| Nyanja | Ujifunzaji wa Mashine | Ujifunzaji wa Mashine |
| Familia | Machine learning | Machine learning |
| Mwaka wa asili≠ | 2018 | 2006 (book); roots in Kriging, 1951) |
| Mwanzilishi≠ | Golan & El-Yaniv; Ruff et al. | Rasmussen, C. E. & Williams, C. K. I. |
| Aina≠ | Self-supervised anomaly/novelty detection | Probabilistic non-parametric model |
| Chanzo asilia≠ | 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 |
| Majina mbadala | SS-OCSVM, Self-supervised SVDD, Self-supervised novelty detection, Pretext-task OC-SVM | GP, Gaussian Process Regression, GPR, Kriging |
| Zinazohusiana≠ | 6 | 3 |
| Muhtasari≠ | 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. |
| ScholarGateSeti ya data ↗ |
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