Also known as: ERM
ERM (Empirical Risk Minimization) is a learning principle that selects the function or model that minimizes loss on the training data. It is a foundational approach in supervised learning and contrasts with methods that explicitly control model complexity or expected generalization risk.
Choosing the function that minimizes loss on the training set. Contrast with structural risk minimization.