on the right track?

[Sendai]

I thought it would be nice to have a way to check if we're on the right track with problems 2-5 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

[Anjoola]

Hi, I got ALMOST the same numbers, except I got 860 support vectors for the first one instead of 861, and as a result my E_in is slightly different than yours. How did you choose your support vectors? Did you just check for or or ?

[hemphill]

I got exactly the same figures as the original poster. I'm using libsvm with the C programming language.

[Sendai]

Originally Posted by Anjoola

How did you choose your support vectors? Did you just check for or or ?

I'm using libsvm via scikit-learn and Python, and it takes care of all of that for you.

For the previous week's homework, I looked for alpha greater than .

Since we're all getting basically the same numbers, I have more confidence that I'm doing it right.

[ivankeller]

Thanks Sendai, That was a good idea.
I'm using scikit-learn 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.

[ilya239]

Originally Posted by Sendai

I thought it would be nice to have a way to check if we're on the right track with problems 2-5 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

Got similar numbers with python and cvxopt. As another check, got four margin support vectors for 0 vs 7, six for 2 vs 8.

[Sendai]

Originally Posted by ilya239

Got similar numbers with python and cvxopt. As another check, got four margin support vectors for 0 vs 7, six for 2 vs 8.

I get three and five respectively using libsvm via Python and scikit-learn.

[Suhas Patil]

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 = 1e-3;
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.

[Suhas Patil]

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.

[butterscotch]

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"