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Old 08-23-2012, 03:25 AM
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htlin htlin is offline
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Location: Taipei, Taiwan
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Default Re: SVM to return probabilistic output

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.
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