Lecture 10: Bayesian Interpretations of Regularization
We describe probabilistic interpretations of Tikhonov regularization, focusing on regularized least squares. We show how ERM, linear RLS and kernel RLS can be derived in a Bayesian framework and discuss implications and possible limitations.
Slides for this lecture: PDF
- Vladimir I. Bogachev Gaussian Measures. Mathematical Surveys and Monographs, Volume 62, American Mathematical Society, 1998.