# Structural risk minimization and SVMs

I know what is SRM but I didn't understand the relation between SRM and SVMs. Can anyone explain me this? Why they say that SVMs rely on a SRM approach? Thank you so much!

1. Minimise the empirical risk $R_\mathrm{emp}$ in each of the function classes
2. Minimise the guaranteed risk $R_\mathrm{g} = R_\mathrm{emp} + \mathrm{complexity}$
When training a support vector machine, you try to find the classification border that minimises the number of misclassifications and at the same time maximises the margin. In other words, you try to minimise $R_\mathrm{emp}$ together with the complexity (larger margin). Therefore you could argue that an SVM minimises $R_\mathrm{g}$ and thus is an example of SRM.