2012/6/18 Ian Goodfellow <[email protected]>: > > On Fri, May 25, 2012 at 10:32 AM, Ian Goodfellow > <[email protected]> wrote: >> OK. I think I see how to do this for binary classification. For the >> multiclass one-vs-one classification, how do we map from the decision >> function to an actual label prediction?
I think the best way to understand how libsvm does its multiclass one-vs-one predictions is to read the C++ source directly: https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/svm/src/libsvm/svm.cpp#L2765 AFAIK nobody in the scikit-learn project has tried to re-implement it in collapsed primal form (for the linear case) using numpy so far. I don't know how to re-implement multi-class predict_probalities using the collapsed weight vector for the linear kernel case and the primal formulation though. The original libsvm implementation of that function is documented here: http://www.csie.ntu.edu.tw/~cjlin/papers/svmprob/svmprob.pdf (I have not read that paper myself). > Also, if I were to make the speedup in libsvm rather than in > scikits-learn (to fix the problem at the source), how hard would it be > to pull the new version of libsvm in scikits? I think contributing a C++ collapsed prediction function that works in the primal for the linear case directly to libsvm would be useful to people other that scikit-learn users. Would be worth contacting the upstream authors if you plan to do so. Upgrading from one libsvm version to another should definitely not be an issue as it has already been done several times in the past (although it's not that trivial because of our support of both dense and sparse representations which is not the case in the original project). The upstream dense variant of the code base is here: http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/#libsvm_for_dense_data -- Olivier http://twitter.com/ogrisel - http://github.com/ogrisel ------------------------------------------------------------------------------ Live Security Virtual Conference Exclusive live event will cover all the ways today's security and threat landscape has changed and how IT managers can respond. Discussions will include endpoint security, mobile security and the latest in malware threats. http://www.accelacomm.com/jaw/sfrnl04242012/114/50122263/ _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
