I'm reading a book on data science and get confused about how the book describes the hinge loss of SVM. Here is a figure from the book on Page 94:
This figure shows the loss function of a NEGATIVE instance. It seems there is no penalty if the instance lies within the margin even on the positive side. Also on Page 95, the author explains:
However, from what I read about SVM, there should be penalty as long as the negative instance lies within the negative margin or on the wrong side. I updated the loss function as below with orange color:
Can someone tell me if I'm correct or not? Thanks!
Updates: In Wikipedia, it says:
Correctly classified points lying outside the margin boundaries of the support vectors are not penalized, whereas points within the margin boundaries or on the wrong side of the hyperplane are penalized in a linear fashion compared to their distance from the correct boundary