[pymvpa] default SVM C parameter

[Thorsten Kranz]

I also tried a grid search with my EEG data, according to the LibSVM
tutorial in exponential steps, but the differences were surprisingly
low.

Greetings,
Thorsten

2011/9/6 J.A. Etzel <jetzel at artsci.wustl.edu>:
> Several years ago I played with using different values of c, by a rigorous
> grid-search and arbitrarily. I've never seen optimizing make enough of a
> difference with fMRI data to justify the increased hassle and time.
>
> Anyone seen anything different?
>
> Jo
>
>
>
> On 9/1/2011 9:03 AM, Mark Lescroart wrote:
>>
>> Hello all,
>>
>> I have two questions about the default C parameter for linear SVM
>> classifiers in pymvpa (/ LibSVM).
>>
>> First, I'd like a little more information about how the parameter is
>> chosen by default. The help says that the default value is -1, and
>> that "In linear kernel, negative values provide automatic scaling of
>> their value according to the norm of the data," but I didn't find
>> that particularly helpful (What does "according to the norm of the
>> data" mean?)
>>
>> Second, does anyone have any experience choosing a C parameter by
>> more rigorous methods (cross-validated parameter selection)? Does
>> choosing an optimal C value make a big difference?
>>
>> THANKS,
>>
>> Mark
>>
>> PS - I'm still using the 0.4 version of pymvpa - has setting of the
>> default C parameter changed since then? I'm using the LibSVM
>> implementation of the LinearCSVMC classifier on MacOS X 10.5.8.
>>
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