[pymvpa] default SVM C parameter

[Mark Lescroart]

Thanks much for the responses, good to know that classification  
results aren't too fragile...

I would like to re-iterate my question, though: does anyone have any  
idea how (specfically) the default C parameter is chosen?

THANKS,

Mark


On Sep 7, 2011, at 12:30 AM, Thorsten Kranz wrote:

> 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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