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Old 03-29-2013, 11:41 PM
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htlin htlin is offline
Join Date: Aug 2009
Location: Taipei, Taiwan
Posts: 601
Default Re: Probability estimate from soft margin SVMs

Originally Posted by sbgaucho View Post
Apologies if this has already been covered either in the forums or in a lecture, but I don't recall it in any lecture and couldn't find anything in the forum.

It seems to me that it would be nice after using SVM to get a probability estimate that a given x (particularly for out of sample x's) corresponds to y=1. For noisy but non linearly separable data it seems like it would be ideal to combine the probabilistic output of a logistic regression with the power of SVM. I googled this and found a couple presentations/references, but it doesn't seem like there is a clear-cut answer. Am I way off base? If not what is the simplest/easiest direction to go in terms of learning about and implementing such a thing? Is it easiest just to use something like libsvm or weka?

SVM with probabilistic outputs is useful for some applications. The most popular technique was proposed from Platt. The technique basically runs a variant of logistic regression to post-process the outputs of SVM. An earlier work of myself improves Platt's proposed algorithm from an optimization perspective:

Hope this helps.
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