方法对比
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| 贝叶斯岭回归× | 岭回归（Ridge Regression）× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族≠ | Bayesian methods | Machine learning |
| 起源年份≠ | 1992 | 1970 |
| 提出者≠ | MacKay, D. J. C. | Hoerl, A.E. & Kennard, R.W. |
| 类型≠ | Probabilistic regularised regression | L2-regularized linear regression |
| 开创性文献≠ | MacKay, D. J. C. (1992). Bayesian Interpolation. Neural Computation, 4(3), 415–447. DOI ↗ | Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67. DOI ↗ |
| 别名 | BRR, Bayesian linear regression with automatic relevance determination, evidence approximation ridge, marginal likelihood ridge | Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization |
| 相关≠ | 3 | 4 |
| 摘要≠ | Bayesian Ridge Regression is a probabilistic formulation of ridge regression, introduced by David J. C. MacKay in 1992, in which the regularisation strength and noise precision are not fixed by the analyst but are instead estimated automatically by maximising the marginal likelihood (evidence) of the observed data. The result is a full posterior distribution over the regression weights together with calibrated predictive uncertainty. | Ridge Regression is an L2-regularized linear regression method, introduced by Arthur Hoerl and Robert Kennard in 1970, that reduces multicollinearity by adding a penalty on the size of the coefficients. It shrinks coefficients toward zero without setting any of them exactly to zero, producing more stable estimates when predictors are highly correlated. |
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