Loss functions for classification: Difference between revisions

Revision as of 15:28, 5 December 2014

Revision as of 15:29, 5 December 2014

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While more commonly used in regression, the square loss function can be re-written as a function <math>\phi(yf(\vec{x}))</math> and utilized for classification. Defined as

:<math>V(f(\vec{x}),y) = (1-yf(\vec{x}))^2</math>

the square loss function is both convex and smooth and matches the 0-1 [[indicator function]] when <math>(yf(\vec{x}))= 0</math> and when <math>(yf(\vec{x})) = 1</math>.

== Hinge Loss ==