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 non-zero, then the probability at the decision boundary will not be 1/2.
Is the reason for needing non-zero 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?
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