My pleasure! On Fri, Sep 4, 2015 at 4:03 PM, Andrew Musselman <[email protected] > wrote:
> Thanks Alex; grateful for the help. > > On Fri, Sep 4, 2015 at 12:59 PM, alxsmac733 . <[email protected]> > wrote: > > > Hi Dmitriy, > > > > That sounds more than reasonable - take as much time as you need. I'll > be > > away for the next two weeks anyway so I won't be able to start working on > > this until I get back should you want me to move forward with the > proposal. > > > > - Alex > > On Sep 4, 2015 1:46 PM, "Dmitriy Lyubimov" <[email protected]> wrote: > > > > > 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 . <[email protected]> > > > 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 < > > > > [email protected] > > > > > 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 . < > [email protected] > > > > > > > > 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 > > > > > > > > > > > > > > > > > > > > >
