Re: SVM to return probabilistic output
Yes. Thank you. Very interesting. I read both papers (well, skimmed some parts) and basically followed but I do have a general question.
I can understand A as a saturation factor or gain, but at first glance B is a little confusing. If B is nonzero, then the probability at the decision boundary will not be 1/2.
Is the reason for needing nonzero B that the mapping from Y>T no longer just maps +1 to 1, and 1 to 0, but rather to two values in (0,1) based on the relative number of +1s to 1s?
