SVM equation from Slides
Prof Yaser.
Even though its not related to book chapter but the slide but thought it made sense here in this discussion. In SVM slides Lecture 14 On page 13 we had just converted the problem to minimization and had minimize Lagrangian(alpha)=.... On page 14 we have maximize Lagrangian w.r.t alpha subject to... How did this change? Also page 15 we convert the maximize problem to minimize problem by inverting the sign. So the jump from first minimize to maximize of lagrangian is not clear. Thanks Uday Kamath 
Re: SVM equation from Slides
Quote:

Re: SVM equation from Slides
thanks for your answer, so Lagrange(w,b,alpha) minimization means w.r.t to minimize w and b and maximize w.rt. alpha.
Also, you mention in the video that the KKT condition, the first one, of replacing the min yn(wXn+b) =1 is equivalent to using the inequality using the slack time square and adjusting. You mention that you will explain that in the Q&A, but no one asked that in Q&A and was wondering if you can here or some place give and explanation of the slack square thing and how min gets changed to non minimum with adding equality. Thanks again for the wonderful lectures and book! Forever indebted! Uday 
All times are GMT 7. The time now is 02:18 PM. 
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.