A related question asked by email
Quote:
Can you please tell me if the following would be a good idea for postprocessing after performing SVM: use the same zspace 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 crossentropy 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 HsuanTien Lin):
Quote:
Postprocessing 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.
