Alex, can you give us a week or so to look it over?
We have been discussing for a while hyperparameter fitting approaches and it is fairly high on our roadmap (crossvalidation is of course an important element of it). We need to figure how it may fit together; but don't get discouraged if we don't get immediately back to you, we need time to digest your proposal. -d On Fri, Sep 4, 2015 at 10:26 AM, alxsmac733 . <ajmoreno1...@gmail.com> wrote: > The fast cross-validation algorithm might be a good place to start as it > may be the most broadly useful. > > Any advice on how to get started would be greatly appreciated - I want to > make sure I do a good job and it fits well with the overall aims of Mahout. > > On Fri, Sep 4, 2015 at 1:12 PM, Andrew Musselman < > andrew.mussel...@gmail.com > > wrote: > > > Sounds interesting; what part would you like to start with? > > > > If you need help getting started we're happy to point you in a good > > direction. > > > > On Fri, Sep 4, 2015 at 9:55 AM, alxsmac733 . <ajmoreno1...@gmail.com> > > wrote: > > > > > Hi everyone, > > > > > > Would there be any interest in adding algebraic classification methods > to > > > Mahout? It's an elegant approach that allows for easy online and > > parallel > > > training as well as fast cross-validation. Below are some links > > describing > > > the approach as well as an existing Haskell package implemented by the > > > author. The first paper does a very good job of explaining the basic > > > concepts clearly and concisely. > > > > > > https://izbicki.me/public/papers/icml2013-algebraic-classifiers.pdf > > > > > > > > > https://izbicki.me/public/papers/tfp2013-hlearn-a-machine-learning-library-for-haskell.pdf > > > https://izbicki.me/ > > > https://github.com/mikeizbicki/HLearn > > > > > > The author saw a very large speed up implementing these techniques when > > > compared with popular existing libraries such as Weka. Aside from the > > > potential performance gains to be had, I think imposing algebraic > > structure > > > provides a nice layer of abstraction over the particular models being > > > implemented. > > > > > > I'd love to hear everyone's feedback on this. Thanks for your time and > > > enjoy your weekends! > > > > > > Alex Moreno > > > > > >
