# Support Vector Machine: A non-probabilistic binary linear classifier

I read that SVM is a supervised learning method, it is also a non-probabilistic binary linear classifier. I understand why it is binary because it classifies our training pattern to two classes $w_1$ and $w_2$ which are labeled as $y_i=+ 1$ and $-1$. And I understand why it is linear because it classifies when the classes are linearly separable. But I don't understand the non-probabilistic part, could someone clarify?

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