Lecture 10: Bayesian Interpretations of Regularization

Charlie Frogner


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

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