Ok! Last question: I saw that the grid search works to find the appropriate
C parameter.
Is it possible to make it work also to find the optimal gamma parameter of
my precomputed RBF kernel?
Regards,
Emeline
2012/5/23 Andreas Mueller <[email protected]>
> **
> Sorry about being unclear.
> It is not possible to use another distance in the RBF kernel. You have to
> precompute it your own.
> But it is possible to do grid search using the precomputed kernel.
>
> Cheers,
> Andy
>
>
> Am 23.05.2012 14:58, schrieb Emeline Landemaine:
>
> I'm not sure to understand: do you mean that this is possible to change
> the distance used within the RBF kernel because I didn't see documentation
> about it?
>
> Thank you
>
> 2012/5/23 Andreas Mueller <[email protected]>
>
>> Hi Emeline.
>>
>> >
>> > I would like to use SVM in scikit-learn with a RBF kernel K(x,y)= exp(
>> > -gamma *d(x,y)^2 ) with d that is not the usual distance but a
>> > weighted sum of other distances.
>> > How can I do so? Do I have to precompute the kernel on my own?
>> Yes, you have to precompute the whole kernel.
>> > Moreover, to determine gamma, I would like to perform cross-validation
>> > on a grid-search but I'm not sure if it's possible with a precomputed
>> > kernel ...
>> >
>> This is supported at least since 0.11, maybe earlier.
>> The code is not very mature yet so if you run into any troubles, please
>> let us know.
>>
>> Cheers,
>> Andy
>>
>>
>>
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