Makes sense
Well from what I understand, just getting the multi-class logistic and svm loss
and the group lasso penalty into scikit-learn seems like a worthwhile
undertaking.
—
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On Tue, Feb 4, 2014 at 10:34 AM, Gael Varoquaux
<gael.varoqu...@normalesup.org> wrote:
> On Tue, Feb 04, 2014 at 10:32:12AM +0200, Nick Pentreath wrote:
>> Are some of the algorithms too cutting edge or not cited enough,
> Yes
>> or some other reason?
> I think that it is good practice to explore new ideas outside of
> scikit-learn. It usually takes a lot of effort and time to figure out
> whether an approach will be a lot of benefits or not.
> Gaël
>> On Tue, Feb 4, 2014 at 10:28 AM, Gael Varoquaux
>> <gael.varoqu...@normalesup.org>
>> wrote:
>> On Tue, Feb 04, 2014 at 09:04:00AM +0100, Alexandre Gramfort wrote:
>> > > Alex had provided me a link to this gist,
>> > > https://gist.github.com/fabianp/3097107 . Sorry for sounding dumb,
>> but
>> is
>> > > this one of the "strong rules"?
>> > yes
>> http://arxiv.org/pdf/1011.2234
>> > > And one last question, what about generalized additive models? Would
>> that be
>> > > a good GSoC project to do?
>> > I am +0 on this now. I suggested MARS/EARTH as there is already some
>> code
>> > which would facilitate success.
>> I would personnally be more excited about merging in the fast logistic
>> regression and SVM from lightning https://github.com/mblondel/lightning.
>> G
>>
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> --
> Gael Varoquaux
> Researcher, INRIA Parietal
> Laboratoire de Neuro-Imagerie Assistee par Ordinateur
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