|
As http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf introduces the mathematica setting libsvm uses a L1-error term and quadratic (that is L2) regularization. Liblinear supports different combinations of L1/L2 errors and regularizations: Look at "introduction" at http://www.csie.ntu.edu.tw/~cjlin/liblinear. So if you want to compare both approaches you should configure liblinear such that it fits libsvms setting. Despite of that difference you may get different results due to different algorithms. In a bad conditioned setting the weights might differ for nearly similiar values of the loss function. Uwe, Thanks. I will look into this more.
(May 16 '11 at 14:54)
Dexter
|
|
Here are some possibilities:
Jrennie, I think both have different objective functions. Let me dig into it more.
(May 16 '11 at 14:11)
Dexter
|
What do you mean when saying "different results" ? Do you get different weights ? How big is the relative difference in the weights ? Or do you get different classifiers concerning their results when applied to test data ?
Uwe, Sorry if I wasn't clear earlier. The accuracies are different. There's a difference of about 11% with libLinear being the higher one.