structural risk minimization

An algorithm that balances two goals: - The need to build the most predictive model (for example, lowest loss). - The need to keep the model as simple as possible (for example, strong regularization). For example, a function that minimizes loss+regularization on the training set is a structural risk minimization algorithm. Contrast with empirical risk minimization.

Practitioners refer to structural risk minimization when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.