2013/2/5 David Lambert <caliband...@gmail.com>: > Hi, > > I'm new to the list so please forgive my trespasses... > > I've nearly completed an implementation of the Extreme Learning Machine (very > fast SLFN with randomly generated hidden units and no iterative tuning) based > on the .14 release for my own use. I'm not sure what state it needs to be in > before I try to integrate it into the package and submit a pull request, and > what level of interest there is, if any.
Hi David, Am I right to assume that the main reference for ELM is http://www.ntu.edu.sg/home/egbhuang/ ? I had never heard of that term but it seems to share a lot of the ideas of random kitchen sinks and kernel approximations: http://berkeley.intel-research.net/arahimi/random-features/ http://scikit-learn.org/dev/modules/kernel_approximation.html Do you have any reference that compares both approaches on non toy datasets? Also there is ongoing work to implement multi layer perceptron: https://github.com/scikit-learn/scikit-learn/pull/1653 I guess that we won't integrate any new work that is a randomized version of MLP before this PR is first done implementing the traditional MLP learning. -- Olivier http://twitter.com/ogrisel - http://github.com/ogrisel ------------------------------------------------------------------------------ Free Next-Gen Firewall Hardware Offer Buy your Sophos next-gen firewall before the end March 2013 and get the hardware for free! Learn more. http://p.sf.net/sfu/sophos-d2d-feb _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
