pac-learning-framework

IN premise — entries/2026/06/21/wiki-Machine_learning-chunk-1.md

Created 2026-06-21T09:55:50+00:00

PAC (Probably Approximately Correct) learning provides the mathematical/statistical framework for describing machine learning; most traditional ML and deep learning algorithms can be described as empirical risk minimization under PAC

Dependents

These beliefs depend on this one:

• IN svm-grounded-in-learning-theory-but-theory-itself-overturned — SVMs are uniquely grounded in statistical learning theory (PAC framework, ERM with hinge loss recovering Bayes-optimal classifier, SRM regularization) — yet the classical generalization theory that grounds them has itself been overturned by double descent and benign overfitting, creating a paradox where SVMs' strongest theoretical virtue (principled learning-theoretic foundation) rests on a framework now known to be incomplete.