View Single Post
  #6  
Old 08-23-2012, 03:25 AM
htlin's Avatar
htlin htlin is offline
NTU
 
Join Date: Aug 2009
Location: Taipei, Taiwan
Posts: 601
Default Re: SVM to return probabilistic output

Quote:
Originally Posted by patrickjtierney View Post
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?
You are very right. My personal interpretation is that B provides an opportunity to calibrate the boundary of SVM for probability estimates. Recall that SVM roots from large-margin and hence the hyperplane is "right in the middle of the two classes." While arguably, for probability estimates, a good hyperplane (of P = \frac{1}{2}) shall be somewhat away from the majority class. So there may be a need to "shift" the hyperplane by B.

Hope this helps.
__________________
When one teaches, two learn.
Reply With Quote