Hinge Loss Function

Hinge loss functionThe shape of is like a hinge, which is also the origin of its name. This loss function mainly exists in support vector machines, which requires not only correct classification, but also a high enough confidence level for the loss to be 0, that is, the hinge loss function has higher requirements for learning.

The formula of the hinge loss function is L ( y ( w * x + b )) = [ 1 – y ( w * x + b )]

Hinge loss is a loss function used for training classifiers, most notably for support vector machines.