structural-risk-minimization-regularization

IN premise — entries/2026/06/21/wiki-Supervised_learning-chunk-2.md

Created 2026-06-21T09:55:53+00:00

Structural risk minimization adds a regularization penalty C(g) to empirical risk: J(g) = R_emp(g) + λC(g), where λ=0 gives pure ERM and large λ gives high bias/low variance

Dependents

These beliefs depend on this one:

• IN structural-risk-minimization-connects-svm-to-bias-variance — Structural risk minimization formally connects SVMs to the bias-variance tradeoff — SRM's regularization penalty λC(g) is precisely the mechanism that engineers the bias-variance balance, and SVMs instantiate this as ERM with hinge loss and L2 regularization, making SVMs the paradigmatic case where learning theory (PAC/SRM) and statistical decomposition (bias-variance) meet in a single coherent framework.