Empirical Risk Minimization with Relative Entropy Regularization: A Generalized Framework for Incorporating Prior Knowledge

The empirical risk minimization (ERM) problem with relative entropy regularization (ERM-RER) is investigated under the assumption that the reference measure can be a general σ-finite measure, not necessarily a probability measure. This generalization allows for greater flexibility in incorporating prior knowledge into the learning process.