So if I have a custom kernel function: CustomKernel(x, y) - I should do
something like:
M = CustomKernel(model.support_vectors_, X)
# if X is a matrix where the rows are test vectors
results = model.predict_proba(M)
?
Is there an example of this somewhere?
Thanks for your help,
Matt
On 8 November 2011 19:05, Andreas Mueller <[email protected]> wrote:
> Hi Matt.
> For testing, you have to specify the kernel values between the support
> vectors and the test vectors.
> Cheers,
> Andy
>
>
> On 11/08/2011 06:50 PM, Matt Henderson wrote:
>
> Hi,
>
> I would just like a quick piece of clarification. Once you have trained
> an SVM with a precomputed kernel, what is the right way to predict from it?
>
> Thanks,
> Matt
>
>
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