Hi,
That's true, in a way I may as well precompute them all- because I never
know which ones are going to come up. It would make sense though in
general not to require such a big calculation.
Cheers,
Matt
On 9 November 2011 11:07, Andreas Müller <[email protected]> wrote:
> **
> 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]> 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
>>
>>
>>
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