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Old 03-30-2013, 12:47 AM
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yaser yaser is offline
Join Date: Aug 2009
Location: Pasadena, California, USA
Posts: 1,478
Default Re: Probability estimate from soft margin SVMs

A related question asked by email

Can you please tell me if the following would be a good idea for post-processing after performing SVM: use the same z-space but instead of maximizing the margin, use logistic regression (in z space) and also allow the width of the logistic function to be a free parameter (let the cross-entropy be the objective function and use gradient descent). The solution from SVM could be used as the initial guess. Would this be a good idea (ie. improve the SVM result)?
and the answer from htlin (my colleague Professor Hsuan-Tien Lin):

Post-processing the outputs of SVM by logistic regression formulation has been explored for getting probabilistic (soft) outputs from SVMs. The formulation comes with two parameters: the width (scaling) of the SVM output as you suggest, and an additional "bias" term. You can check

and the earlier work of John Platt for some additional information. Hope this helps.
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