In practice , machine learning algorithms cope with that either by employing a convex approximation to the 0-1 loss function ( like hinge loss for Support vector machine ) , which is easier to optimize , or by imposing assumptions on the distribution mathP ( x , y ) / math ( and thus stop being agnostic learning algorithms to which the above result applies ) .