
#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?
I too am getting Ein of 0.35 for 0to7 classification. What is this "sum_node" structure Suhas mentions?

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