Also known as: SRM
SRM (Structural Risk Minimization) is a learning principle that balances predictive performance against model complexity. It seeks models that fit training data well while remaining simple enough to generalize to unseen data.
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.