Quote:
Originally Posted by sbgaucho
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 clearcut 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?
Thanks

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 postprocess the outputs of SVM. An earlier work of myself improves Platt's proposed algorithm from an optimization perspective:
http://www.csie.ntu.edu.tw/~htlin/pa.../plattprob.pdf
Hope this helps.