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

University of Michigan - Ann Arbor

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

Empirical Risk Minimization

Goodfellow, I., Bengio, Y., & Courville, A. (2016). $\mathit{Deep \ Learning.}$ MIT Press. Retrieved from [www.deeplearningbook.org](https://www.deeplearningbook.org) 

Deep Learning

Disadvantage of ERM(Empirical risk minimization)

Design structural risk minimization terms in order to decrease the model complexity and increase the predictability of the model.

Basic Methods to Make Up with ERM

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

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