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Old 04-04-2013, 02:17 PM
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yaser yaser is offline
Join Date: Aug 2009
Location: Pasadena, California, USA
Posts: 1,478
Default Re: SVM equation from Slides

Originally Posted by udaykamath View Post
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!
The slack argument is probably available online in writeups about KKT. The basic idea is to add a squared variable s^2 to one side of an inequality to make it equality, and because it is squared, there are no resrictions on the value of s itself.
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