How does SVM differ from perceptron learning?

SVM maximizes margin globally while perceptron only finds any separating hyperplane.

Perceptron updates weights iteratively until it finds a separating hyperplane if data is linearly separable. It does not maximize margin. SVM, however, solves a convex optimization problem to find the maximum margin separator, improving generalization significantly.