> Okay, that sounds reasonable to me too. > It appears to me that it might be in everyone interest if I apply for > a different project. I'm considering "Coordinated descent in linear > models beyond squared loss (eg Logistic)" > I'm currently working on a p>>N problem using the R scout package, > where I’m running into "out of memory" and performance issues due to > R's memory restrictions. I could imagine that scikit-learn could > really profit I we could get around this problems.
hum it's seems surprising that a coordinate descent procedure blows up the memory but i'll have to read the paper. When I find the time … I had more in mind the glmnet approach for multinomial logistic regression which scales pretty well AFIAK > In short, I think it could be interesting to implement the scout method too: > "We show that ridge regression, the lasso, and the elastic net are > special cases of covariance-regularized regression" > http://www-stat.stanford.edu/~tibs/ftp/WittenTibshirani2008.pdf being more general is neat but the price you might have to pay is less efficiency for the simpler problems. Alex ------------------------------------------------------------------------------ This SF email is sponsosred by: Try Windows Azure free for 90 days Click Here http://p.sf.net/sfu/sfd2d-msazure _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
