When the model is probability distribution and the loss function is log-likelihood loss function, ERM is actually a maximum likelihood estimation.

University of Michigan - Ann Arbor

The training process based on minimizing this average training error is known
as empirical risk minimization.

Empirical Risk Minimization

1.Empirical risk minimization is prone to overfitting.
2.Many useful loss fucntions have no derivatives.

Disadvantage of ERM(Empirical risk minimization)

Basic case of ERM

Structural risk minimization (SRM) is a machine learning principle that addresses empirical risk minimization's susceptibility to overfitting by balancing the empirical risk against model complexity.

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