Hinge Loss

The hinge loss is used for "maximum-margin" classification, most notably for support vector machines (SVMs).

For an intended output t = ±1 and a classifier score y(raw score), the hinge loss of the prediction y is defined as

l(y) = max(0, 1 - t*y)

It can be seen that when t and y have the same sign (meaning y predicts the right class) and |y| \ge 1, the hinge loss l(y) = 0, but when they have opposite sign, l(y) increases linearly with y (one-sided error).

如下图t=1时

ml-hinge-loss

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