According to the ME principle, the optimal parametric form of the model of our data, taking into account the constraints imposed by the feature functions, is a log linear combination of these functions.
for our classification problem, we have: where c i is the class (c 0 ="parallel", c 1 ="not parallel"), Z(sp) is a normalization factor, and f ij are the feature functions (indexed both by class and by feature).