Bayesian interpretation of kernel regularization

Sat, 18 Jul 2026 09:47:51 +0000

Definition

This concept establishes that minimizing a regularized risk functional with a specific kernel is equivalent to finding the maximum a posteriori (MAP) estimate in a Bayesian framework. Specifically, it interprets the regularization term as a log-prior over functions, often corresponding to a Gaussian Process prior. This connection allows practitioners to apply Bayesian uncertainty quantification techniques to deterministic kernel methods, providing probabilistic predictions and insights into model confidence.

Summary

A theoretical framework linking kernel methods like SVMs to Gaussian Processes under a Bayesian prior assumption.

Kernels on English AI Terms Dictionary

Bayesian interpretation of kernel regularization

Definition

Summary