Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Učení metrik× | Gaussovský proces× | |
|---|---|---|
| Obor | Strojové učení | Strojové učení |
| Rodina | Machine learning | Machine learning |
| Rok vzniku≠ | 2003 (foundational); refined 2009 (LMNN) | 2006 (book); roots in Kriging, 1951) |
| Tvůrce≠ | Xing, E. P.; Jordan, M. I.; Russell, S.; Ng, A. Y. | Rasmussen, C. E. & Williams, C. K. I. |
| Typ≠ | Representation learning / supervised distance optimization | Probabilistic non-parametric model |
| Původní zdroj≠ | Xing, E. P., Jordan, M. I., Russell, S., & Ng, A. Y. (2003). Distance metric learning with application to clustering with side-information. In Advances in Neural Information Processing Systems (NIPS), 16, 505–512. link ↗ | Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9 |
| Další názvy | Distance Metric Learning, Similarity Learning, DML, Representation Learning via Distance | GP, Gaussian Process Regression, GPR, Kriging |
| Příbuzné≠ | 5 | 3 |
| Shrnutí≠ | Metric learning is a machine-learning framework that trains a distance or similarity function from data so that semantically similar examples end up close together in the learned space while dissimilar examples are pushed apart. Unlike fixed distances such as Euclidean, the learned metric adapts to the structure of the task, making downstream classifiers, clusterers, and retrieval systems significantly more accurate. | 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. |
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