[SciPy-dev] Google Summer of Code and scipy.learn (another trying)

[Matthieu Brucher]

>
> if I got it right, you just want to train on 1 dataset (name it
> training) and transfer (predict) another (name it validation) (which
> might have just a single sample/image)



Exactly.


(...)



thanks for the example, I'll try that :)


>    is now needed to be able to get the underlying cost function, more and
> >    more publications use it to show interesting results, and it is what
> >    could be thought as a state in your framework ?).
>
> ah, so you want to see the actual value which was used to decide on the
> label. Eg for linear SVM it would be w*x+b value, right?
> For those we actually have already state variable "values" (as opposed
> to labels), but we are yet to "connect" it to the real computed value I
> believe since we haven't used it yet (actually now it is linked to
> prediction probability as it is implemented within libsvm)...



That's what I want, yes. What is the value it contains if not the wx+b one ?


Also please have a look at doc/examples/pylab_2d.py which plots
> probability maps for few classifiers ;-)



Thanks for the tip, this may be of interest as well :)


> This is expecting
> >    when testing one individual versus two groups (it is done in anatomic
> >    brainimaging). Could you add an exemple with this ?
>
> shouldn't you simply have a binary classifier to classify between two
> groups?



Yes, that's what you usually have, but in some case you want to have the
difference between the sample and the other class.


>    I also know that Windows is not very tested for the moment, I hope the
> >    mix with scikits.learn will help promoting your tool and fix the
> >    different bugs that will be found (or not if there are no bugs :D)
> with
> >    other platforms.
>
> Yeah -- eventually we should try it on some proprietary OS like Windows
> but we aren't brave enough :-)


:)

Thanks for the intel

Matthieu
-- 
French PhD student
Website : http://matthieu-brucher.developpez.com/
Blogs : http://matt.eifelle.com and http://blog.developpez.com/?blog=92
LinkedIn : http://www.linkedin.com/in/matthieubrucher
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