Hi list,
I'm more than +1 for online learning, it could be a killing feature of the
scikit !
I also like the first suggestion of Andreas, about Multinomial Logistic
regression. I think there is interesting work to do in the junction with
Bayesian statistics and priors.
Vincent
2012/1/19 Alexandre Gramfort <[email protected]>
> i've created the wiki page to organize what was suggested and so
> people can volunteer for mentoring.
>
>
> https://github.com/scikit-learn/scikit-learn/wiki/A-list-of-topics-for-a-google-summer-of-code-%28gsoc%29-2012
>
> Alex
>
> On Thu, Jan 19, 2012 at 8:38 AM, Peter Prettenhofer
> <[email protected]> wrote:
> >>>> [..]
> >>>>> - Structured SVM / CRF learning
> >>>>> This is a big one. Not sure what other people think of it.
> >>>>> I think having a structured SVM would be great.
> >>>>>
> >>>
> >>>> +100 on this one...
> >>>>
> >>> For this, do we need to have our own SVM solver? This is a naive
> >>> question, I have never looked at structured SVM.
> >>>
> >>> This seems to me as a fairly challenging project.
> >>>
> >>>
> >> This is quite definitely a challenging project.
> >> This should only be given to someone with a fair understanding
> >> of the topic.
> >>
> >> There are several options, as I tried to say in my initial post:
> >> 1) bindings for an existing structured SVM.
> >> 2) bindings for a smart solver with structured svm code by us
> >> 3) using SGD for solving. This means "having our own SVM solver"
> >> -- but we already got one in SGDClassifier.
> >> 4) write a solver using cutting plane or bundle methods (not quite
> >> sure if this is a good idea)
> >>
> >> From my point of view 1) would be most desirable, though
> >> it is not quite clear how possible it is.
> >> 2) and 3) are definitely good options.
> >>
> >> 4) is probably to much for a GSoC project.
> >>
> >> Having this feature might get us a LOT of attention.
> >> But this is really not a simple project.
> >
> > I agree that this is probably too ambitious for a GSoC: first we have
> > to decide whether to build a generic structured prediction framework
> > (similar to [1]) or an application specific solution (e.g.
> > linear-chain CRF which are very popular in NLP).
> >
> > If we choose the former I think we have to implement it ourselves. If
> > we choose the latter there are a number of great open source
> > implementations - e.g crfsuite (Jacob has started working on a sklearn
> > conform python wrapper a while ago).
> >
> > [1] http://tfinley.net/software/svmpython2/
> >
> > --
> > Peter Prettenhofer
> >
> >
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