Bayesian interpretation of kernel regularization

Bayesian interpretation of kernel regularization examines how kernel methods in machine learning can be understood through the lens of Bayesian statistics, a framework that uses probability to model uncertainty. Kernel methods are founded on the concept of similarity between inputs within a structured space.