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 
Re: Probability estimate from soft margin SVMs
Quote:
http://www.csie.ntu.edu.tw/~htlin/pa.../plattprob.pdf Hope this helps. 
Re: Probability estimate from soft margin SVMs
A related question asked by email
Quote:
Quote:

All times are GMT 7. The time now is 10:08 AM. 
Powered by vBulletin® Version 3.8.3
Copyright ©2000  2020, Jelsoft Enterprises Ltd.
The contents of this forum are to be used ONLY by readers of the Learning From Data book by Yaser S. AbuMostafa, Malik MagdonIsmail, and HsuanTien Lin, and participants in the Learning From Data MOOC by Yaser S. AbuMostafa. No part of these contents is to be communicated or made accessible to ANY other person or entity.