# The difference between logistic regression and support vector machines?

I know that logistic regression finds a hyperplane that separates the training samples. I also know that Support vector machines finds the hyperplane with the maximum margin.

My question: is the difference then between logistic regression (LR) and support vector machines (SVM) is that LR finds any hyperplane that separates the training samples while SVM finds the hyperplane with the maximum margin? Or am I wrong?

Note: recall that in LR when $\theta \cdot x = 0$ then the logistic function gives $0.5$. If we assume $0.5$ as a classification threshold, then $\theta \cdot x = 0$ is a hyperplane or a decision boundary.

• – Ryan Zotti Mar 14 '15 at 17:38