Hi Matt.
I had a similar setup as you did once. As my kernel was very slow, it
helped a lot -
though I precomputed all kernel values.
I'm pretty sure the underlying libsvm supports only providing the kernel
values
at the support vectors. I'm not sure why this is not supported by
sklearn at the moment.
On the other hand, that wouldn't really help for your use case.
If you use different classifiers and parameter combinations, each of the
classifiers
will have different sets of support vectors and you could not precompute
the kernel for all of them.
Cheers,
Andy
On 11/09/2011 11:47 AM, Matt Henderson wrote:
> Hi Andy,
>
> Thanks for the example. I actually started experimenting with
> defining my own python function kernel, which caches its results so
> that it is fast once it has already been called once with the same
> input. (Useful since I am training on the same data multiple
> classifiers and comparing different parameters..)
> I noticed that at testing, the kernel gets called with the test data
> and ALL of the training data, like you mentioned.
> That could make things a lot slower than they need to be, right? I
> thought one of the main advantages of SVMs is the sparse
> representation of the training set which they derive- and this is
> being lost apparently.
>
> Cheers,
> Matt
>
> On 9 November 2011 10:25, Andreas Müller <[email protected]
> <mailto:[email protected]>> wrote:
>
> Hi Matt.
> Did you figure it out yet?
>
> Here is an example:
> https://gist.github.com/1351047
> It seems that at the moment, you have to use the whole training set to
> generate the kernel at test time.
> Not sure why, maybe for ease of use.
>
> Can anyone comment on that?
>
> Cheers,
> Andy
>
>
>
> ------------------------------------------------------------------------------
> RSA(R) Conference 2012
> Save $700 by Nov 18
> Register now
> http://p.sf.net/sfu/rsa-sfdev2dev1
> _______________________________________________
> Scikit-learn-general mailing list
> [email protected]
> <mailto:[email protected]>
> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
>
>
>
> ------------------------------------------------------------------------------
> RSA(R) Conference 2012
> Save $700 by Nov 18
> Register now
> http://p.sf.net/sfu/rsa-sfdev2dev1
>
>
> _______________________________________________
> Scikit-learn-general mailing list
> [email protected]
> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
------------------------------------------------------------------------------
RSA(R) Conference 2012
Save $700 by Nov 18
Register now
http://p.sf.net/sfu/rsa-sfdev2dev1
_______________________________________________
Scikit-learn-general mailing list
[email protected]
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
