Hi Nick.
Afaik no one plans to work on that at the moment.
I was thinking about it some while ago, but then decided not to do it.
By MKL you mean learning the weights of different kernels, right?
Or do you mean inducing some group sparsity on the different kernels?
From what I heard recently, learning weights did not prove to be very
useful
in practice (on its own). Do you have an example where learning weights
while learning the classifier helped, compared to just adding kernels
(= concatenating features). I would be very interested :)
About implementing them: I'm not sure that would be an easy thing to do.
I think you would have to dig quite deeply into LibSVM to do this
efficiently.
Cheers,
Andy
On 02/20/2012 03:36 PM, Nicholas Pilkington wrote:
I was wondering if there were any immediate plans to implement
Multiple Kernel SVMs in sklearn? There is a lots of SVM functionality
and it would be nice to extend to MKL.
If not what would be the best way to get involved in implementing them.
Nick
--
Nicholas C.V. Pilkington
University of Cambridge
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