Quote:
Originally Posted by BojanVujatovic
Hi, I want to compliment and thank Professor and others for the wonderful set of lectures and the textbook which explain the subject of Machine Learning with extraordinary ease and clarity.
I have a question about the Generalization result of SV's, in Lecture 14, slide 20. It says that:
I don't understand how was this bound derived (from something like the VC bound or is it an observation)? Also I am interested in knowing does it hold for any classification problem that we apply SV's for?

Thank you for your kind words. The proof of the inequality makes a number of assumptions. You can find a version of it in Vapnik's book "Statistical Learning Theory."