What is Structural Risk Minimization (SRM) in SVM?

SRM is a principle that balances model complexity and training error to improve generalization.

SVM implements SRM by minimizing a bound on generalization error rather than just training error. It combines empirical risk (hinge loss) with a regularization term controlling margin size. This tradeoff ensures that the model does not overfit training data.