Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Regulārizēts Gausa process× | Regularizēta atbalsta vektoru mašīna× | |
|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2006 (canonical formulation); kernel regularization roots 1990s | 1995–2004 |
| Autors≠ | Rasmussen, C. E. & Williams, C. K. I. | Cortes, C. & Vapnik, V. (soft-margin SVM); Zhu et al. (L1-SVM) |
| Tips≠ | Probabilistic kernel model with regularization | Regularized discriminative classifier / regressor |
| Pirmavots≠ | Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9 | Cortes, C. & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297. DOI ↗ |
| Citi nosaukumi | Regularized GP, GP with noise regularization, sparse regularized Gaussian process, regularized Gaussian process regression | Regularized SVM, L1-SVM, L2-SVM, penalized SVM |
| Saistītās | 4 | 4 |
| Kopsavilkums≠ | A Regularized Gaussian Process (GP) is a probabilistic kernel-based model that places a prior over functions and explicitly controls overfitting through a noise regularization parameter — the observation noise variance — that prevents the model from memorizing training labels. It produces calibrated uncertainty estimates alongside predictions, making it uniquely suited to small or expensive datasets where knowing how confident the model is matters as much as the prediction itself. | Regularized Support Vector Machine extends the classic SVM by explicitly controlling the trade-off between margin maximization and training error through an L1 or L2 penalty parameter. The soft-margin formulation introduced by Cortes and Vapnik in 1995 is itself a regularized model, and later L1-SVM variants additionally promote feature sparsity, enabling automatic variable selection in high-dimensional settings. |
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