#1




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 
#2




Re: SVM equation from Slides
Thank you for asking. In slide 13, minimization is w.r.t. some of the variables, and for the rest of the variables it is maximization (per Lagrange/KKT method). The minimization has already been done in the derivation in slide 13, so only the maximization part remains in slide 14.
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#3




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 
#4




Re: SVM equation from Slides
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