Can you interpret probabilistically the output of a Support Vector Machine? I am trying to build a binary classification system using different classification algorithms like random forests, support vector machines, AdaBoost. I want to use the output of these classifiers to visualize a score. For example, when using random forests, I would like to use the probability of a sample belonging to class A to build a score from 0 to 100. Given that random forests output a probability (from 0 to 1) using it as the score is intuitive (I would just multiply it by 100). However, given that SVMs output a classification but not a probabilistic output (i.e. distance to the hyperplane, but not probability), would it be legitimate to use the distance to the hyperplane as some sort of "pseudo probability"?  I would, for example, do max-min scale on the distance to the hyperplane for all samples so all distances are scaled from 0 to 1. 

I want to be sure that I can use the distance to the hyperplane as a pseudo-probability and that this pseudo-probability is comparable to the probability to belong to a given class outputted by the random forest. For example, that a sample with a probability of .80 of belonging to class A is the same than another sample of a (min-max transformed) probability of belonging to class A according to the SVM.
One standard way to obtain a "probability" out of a SVM is to use Platt scaling.  See, e.g., this Wikipedia page and this question on Stats.SE.  Platt scaling involves fitting a logistic regression model to predict the "probability", based on the distance to the hyperplane.