#1




on the right track?
I thought it would be nice to have a way to check if we're on the right track with problems 25 without giving away the answers. I ran SVM (with the polynomial kernel) for a couple of cases and pasted the results below. Are others getting the same numbers?
0 vs 7 classifier, C=0.01, Q=2 number of support vectors = 861 = 0.071778 = 0.063241 2 vs 8 classifier, C=0.1, Q=3 number of support vectors = 721 = 0.234878 = 0.291209 
#2




Re: on the right track?

#3




Re: on the right track?
I got exactly the same figures as the original poster. I'm using libsvm with the C programming language.

#4




Re: on the right track?

#5




Re: on the right track?
Thanks Sendai, That was a good idea.
I'm using scikitlearn too, a pretty nice python module. Your results helped me to figure out that I needed to set the parameters gamma and coef0 in sklearn.svm.SVC(...) to 1. These parameters don't appear in the lecture. Now I've got the same results. 
#6




Re: on the right track?
Quote:

#7




Re: on the right track?
I get three and five respectively using libsvm via Python and scikitlearn.

#8




Re: on the right track?
I'm trying libsvm through C, with following parameters:
param.svm_type = C_SVC; param.kernel_type = POLY; param.degree = 2; param.gamma = 1; param.coef0 = 1; param.nu = 0.5; param.cache_size = 200; param.C = 0.01; param.eps = 1e3; param.p = 0.1; param.shrinking = 1; param.probability = 0; param.nr_weight = 0; param.weight_label = NULL; param.weight = NULL; but getting Ein as 0.350 with 0 versus 7 classification. Also unable to find good explaination of these parameters anywhere. Any help? Thanks in advance. 
#9




Re: on the right track?
I found the issue...thanks for reply from buttterscotch. The problem was with the way I was initializing 'svm_node' structure after reading the training data.

#10




Re: on the right track?
Seems good to me. Are you getting the same number of support vectors with Sendai's post? You might want to verify how you calculate the error. The sv_coefficients are not just "alpha", but "y*alpha"

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