SVM regularization - minimizing margin?
You are minimizing the entire loss equation. If it contains regularization, you force the weights to be small too. Having small weights is favorable characteristic because the algorithm is not focusing strongly on one feature, all happen to be important, so the risk of overfitting to some feature is smaller. 

So it is really some tradeoff as always, between the big margin but small focus on dataset distinct examples.