On Wed, Oct 12, 2011 at 8:09 PM, Alexandre Passos <[email protected]> wrote:
> Does anyone know how to implement parameter averaging without touching > every feature at every iteration? With things like CRFs you easily > have millions of features, only a few hundred active per example, so > it's a pain to touch everything all the time. In the page he mentions > that > > Both the stochastic gradient weights and the averaged weights are > represented using a linear transformation that yields efficiency gains > for sparse training data. > > Does anyone know what format this is? I'm not sure if it's the trick used by Bottou but this comment by Peter should be helpful: https://github.com/scikit-learn/scikit-learn/pull/162#issuecomment-1129889 Mathieu ------------------------------------------------------------------------------ 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. Business sense. IT sense. Common sense. http://p.sf.net/sfu/splunk-d2d-oct _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
