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Old 03-30-2013, 12:47 AM
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yaser yaser is offline
Caltech
 
Join Date: Aug 2009
Location: Pasadena, California, USA
Posts: 1,477
Default Re: Probability estimate from soft margin SVMs

A related question asked by email

Quote:
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):

Quote:
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

http://www.csie.ntu.edu.tw/~htlin/pa.../plattprob.pdf

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