Equivalence of empirical risk minimization to regularization on the family of $f- ext{divergences}$

HIGHLIGHTS

• What: Under the current assumptions, 󰀃 󰀄 the objective of this example is to show that B=t󰂏Q,z, ∞.
• Who: Francisco Daunas and collaborators from the Empirical Risk Minimization (ERM) is a fundamental principle in machine learningIt is a tool for selecting a model from a given set by minimizing the empirical risk, which is the average loss or error induced by such a model on each of the labeled patterns available in the training dataset [1], [2]. In a nutshell, ERM aims to find a model that performs well on a given training dataset. However . . .
  

   

  

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