How does SVM generalize better than many other classifiers?

SVM generalizes well because it maximizes the margin, reducing model complexity and overfitting risk.

Generalization in SVM is strongly tied to structural risk minimization. By maximizing the margin between classes, SVM reduces the VC dimension of the classifier, which theoretically improves generalization bounds. Only support vectors influence the model, further reducing sensitivity to noise in non-critical regions of the dataset.