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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