方法证据记录
Bayesian Ridge Regression
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.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Bayesian Ridge Regression (MacKay Probabilistic Regularisation)
分类方法记录 · bayesian / machine-learning
- MacKay, D. J. C. (1992). Bayesian Interpolation. Neural Computation, 4(3), 415–447. · DOI 10.1162/neco.1992.4.3.415
- Bishop, C. M. (2006). Pattern Recognition and Machine Learning (Ch. 3). Springer. · ISBN 978-0-387-31073-2
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