On Sun, Nov 20, 2011 at 3:56 PM, Alexandre Gramfort <[email protected]> wrote: >> 2. Gaussian process w. Expected Improvement global optimization. >> This is an established technique for global optimization that has >> about the right scaling properties to be good for hyper-parameter >> optimization. I think you probably can't do much better than a >> Gaussian Process (GP) with Expected Improvement (EI) for optimizing >> the parameters of say, an SVM, but we can only try and see (and >> compare with the variety of other techniques for global optimization). >> The scikit already has GP fitting in it, scipy has good optimization >> routines, so why not put them together to make a hyper-parameter >> optimizer? I think this would be a good addition to the scikit, and >> not too hard (the hard parts are already done). > > can you point us to some pdfs ? or maybe write some kind of pseudo code?
Eric Brochu's thesis: chapter 2 is very readable, gives lots of good reference as well. > And as usual pull request / patch welcome :) Let me work out the bugs in hyperopt's GP optimization first, and then maybe we can talk more about it at NIPS. - James ------------------------------------------------------------------------------ All the data continuously generated in your IT infrastructure contains a definitive record of customers, application performance, security threats, fraudulent activity, and more. Splunk takes this data and makes sense of it. IT sense. And common sense. http://p.sf.net/sfu/splunk-novd2d _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
