Edward and Williams, Christopher K¶
Rasmussen, C. E., & Williams, C. K. I. (2006). Edward and Williams, Christopher K. Gaussian Processes for Machine Learning.
Cited by¶
1 citation across 1 artifact.
Each citation links to the sentence it supports in the citing article.
Domain-specific¶
- Bayesian Interpretation of Kernel Regularization
- When kernels, means, scale parameters, and normalization conventions are matched, the RKHS minimizer equals the GP posterior mean
This sourceChapter 6.2 derives the regularization-network solution and observes that it is exactly the GP predictive mean, with the regularization weight tied to the noise variance - the standard statement of the equality this sentence asserts.
- When kernels, means, scale parameters, and normalization conventions are matched, the RKHS minimizer equals the GP posterior mean
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