Quote:
Originally Posted by patrickjtierney
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?

You are very right. My personal interpretation is that
provides an opportunity to calibrate the boundary of SVM for probability estimates. Recall that SVM roots from largemargin and hence the hyperplane is "right in the middle of the two classes." While arguably, for probability estimates, a good hyperplane (of
) shall be somewhat away from the majority class. So there may be a need to "shift" the hyperplane by
.
Hope this helps.