Parameter Norm Penalties

Parameter norm penalties are techniques used in machine learning and optimization to regularize models by discouraging overly complex solutions. These penalties work by adding a term to the loss function that is proportional to the norm of the model parameters, such as L1 (lasso) or L2 (ridge) norms. By applying these penalties, models are encouraged to maintain smaller parameter values, which can help prevent overfitting and improve generalization to unseen data. This approach is particularly useful in high-dimensional spaces where the risk of overfitting is significant, ensuring that the model remains robust and interpretable.