# hinge loss function:

Using this syntax, we can put it all together, obtaining the hinge loss function:

L_{i} = \sum_{j \neq y_{i}} max(0, s_{j} - s_{y_{i}} + 1)
Note: I’m purposely skipping the regularization parameter for now. We’ll return to regularization in a future post once we better understand loss functions.

http://www.pyimagesearch.com/2016/09/05/multi-class-svm-loss/