On Sat, Nov 19, 2011 at 09:15:43PM -0500, James Bergstra 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.
Without knowing that this was an established technique, I had been thinking about this for quite a while. I am thrilled to know that it actually works, and would be _very_ interested about having this in the scikit. Let's discuss it at the sprints. With regards to the random sampling, I am a bit worried that the results hold for a fair amount of points, and with a small amount of points (which is typically the situation in which many of us hide) it becomes very sensitive to the seed. Thanks for your input, James, Gael ------------------------------------------------------------------------------ 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
