> In my opinion, Adagrad is still on the lower side in terms of number of > citations (currently 107 according to Google) for inclusion into scikit-learn. > So unless there is strong evidence that it outperforms other solvers (e.g., in > computer vision or NLP papers that use Adagrad), I'm personally -1 for its > inclusion into scikit-learn.
Agreed. Also, there is currently a lot of progress on improving stochastic gradient based solvers. In particular at the latest NIPS and ICML. Ideally, I think that it is best to wait a little bit for the dust to settled down and then implement what comes out as the best option (I think that my favorite is SAG, but let's wait and see). ------------------------------------------------------------------------------ Rapidly troubleshoot problems before they affect your business. Most IT organizations don't have a clear picture of how application performance affects their revenue. With AppDynamics, you get 100% visibility into your Java,.NET, & PHP application. Start your 15-day FREE TRIAL of AppDynamics Pro! http://pubads.g.doubleclick.net/gampad/clk?id=84349831&iu=/4140/ostg.clktrk _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
