How does SVM relate to VC dimension and statistical learning theory?

SVM controls VC dimension by maximizing margin, improving generalization guarantees.

VC (Vapnik–Chervonenkis) dimension measures a model's capacity to fit diverse datasets. In SVM, maximizing margin effectively reduces VC dimension, even in high-dimensional spaces. This is a core result in statistical learning theory: a larger margin leads to tighter generalization bounds.