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  #11  
Old 09-16-2012, 09:09 PM
patrickjtierney patrickjtierney is offline
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Default Re: Question 13

Using x = [1 0;0 1;0 -1.00002; -1 0; 0 2.0001; 0 -2; -2 0]; I still get one less s.v. for qp than libsvm (ie same values I get without perturbing). This remains the case when only perturbing one s.v. The most notable change is that the second weight entry grows, although the first and b also change.

Also, thanks to fgpancorbo for the code for getting w & b from libsvm. Useful for the future.
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  #12  
Old 09-17-2012, 07:47 AM
MLearning MLearning is offline
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Default Re: Question 13

Quote:
Originally Posted by patrickjtierney View Post
Using x = [1 0;0 1;0 -1.00002; -1 0; 0 2.0001; 0 -2; -2 0]; I still get one less s.v. for qp than libsvm (ie same values I get without perturbing). This remains the case when only perturbing one s.v. The most notable change is that the second weight entry grows, although the first and b also change.

Also, thanks to fgpancorbo for the code for getting w & b from libsvm. Useful for the future.
@patrickjtierney,

In z space X6 and X7 map to the same point in z space, i.e, X6 (0, -2) and X7 (-2,0) map to (3,5). I wonder if this has any effect on the computation.
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  #13  
Old 09-17-2012, 10:26 AM
patrickjtierney patrickjtierney is offline
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Default Re: Question 13

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Originally Posted by MLearning View Post
@patrickjtierney,

In z space X6 and X7 map to the same point in z space, i.e, X6 (0, -2) and X7 (-2,0) map to (3,5). I wonder if this has any effect on the computation.
I noticed that in Q12, but I believe that the z-space is defined by the polynomial kernel in Q13, and not the mapping from Q12. This can be seen in slide 10 of week 15 where g(x) is specified.
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  #14  
Old 09-17-2012, 03:01 PM
MCN12 MCN12 is offline
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Default Re: Question 13

Using Matlab libsvm I perturbed [0,-1] to [0,-0.94] and it reduced the number of support vectors by one. W and b agree with what others have seen for libsvm.
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  #15  
Old 09-17-2012, 06:32 PM
fgpancorbo fgpancorbo is offline
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Default Re: Question 13

I used libsvm and got this question right.
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  #16  
Old 09-17-2012, 09:48 PM
Anton Khorev Anton Khorev is offline
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Default Re: Question 13

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Originally Posted by yaser View Post
Interesting. Is the hypothesis g identical?
Predictions of qp and libsvm are identical (for 10000 uniformly distributed samples on x1,x2 = [-5,5]).
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  #17  
Old 09-17-2012, 09:58 PM
JohnH JohnH is offline
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Default Re: Question 13

As a quick (read minimal effort) check of model equivalence, I compared the predicted results of 1,000,000 randomly selected points within [-3,3][-3,3] using the support vectors from both Octave/QP and Python/libsvm. Only three points were classified differently despite the difference in the number of support vectors returned by the two approaches. I'm certain that an analytical comparison of the support vectors would prove their equivalence; however, it hardly seems necessary given the empirical results.
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  #18  
Old 06-01-2013, 07:32 AM
marco.lehmann marco.lehmann is offline
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Default Re: Question 13

I had quite some problems with solving this problem and it forced me to play around with different approaches (qp, libsvm). One more approach to consider is this: Lecture 15, slide 5:
In the case of the kernel used in exercice 13, there is a corresponding transformation, given explicitly on that slide. So why not giving it a try?
I got some confidence in the result after reading the slides title.
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