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Old 07-28-2013, 04:42 PM
hsolo hsolo is offline
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Join Date: Jul 2013
Posts: 12
Default Re: computing w, b for soft margin SVM

Quote:
Originally Posted by yaser View Post
1. All of them for computing {\bf w}, since any vector with \alpha>0 will contribute to the derived solution for {\bf w}.

2. Only margin SV's for computing b, since we need an equation, not an inequality, to solve for b after knowing {\bf w}.
Is the 'heuristic' number of parameters (the VC dimension proxy) to be used while reasoning about generalization then the number of margin support vectors << the number of all support vectors?

When we use kernel functions with soft SVMs (problem 2 etc), where there is no explicit w, does the above translate to :
* 1==> Use all support vectors to compute the sigma term in the hypothesis function g()
* 2==> Use only margin support vectors for b (which is also used in g()

I was wondering if this aspect was covered in the lecture or any of the additional material -- I seem to have missed.
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