Probability estimate from soft margin SVMs
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
