>>> [..] >>>> - 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 ------------------------------------------------------------------------------ Keep Your Developer Skills Current with LearnDevNow! The most comprehensive online learning library for Microsoft developers is just $99.99! Visual Studio, SharePoint, SQL - plus HTML5, CSS3, MVC3, Metro Style Apps, more. Free future releases when you subscribe now! http://p.sf.net/sfu/learndevnow-d2d _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
