Go ahead and do the change. Otherwise I can work on it tomorrow. Jörn
On Tue, Aug 29, 2017 at 4:38 PM, Dan Russ <[email protected]> wrote: > Hi Jörn, > I don’t see a problem with it. Make sure the default is set to the > current value. Are you making the fix? I could get to it later tonight. > Daniel > >> On Aug 29, 2017, at 10:32 AM, Joern Kottmann <[email protected]> wrote: >> >> Hi Daniel, >> >> do you see any issue if we expose LLThreshold and allow the user to >> change it via training parameters? >> >> Jörn >> >> On Sat, Aug 26, 2017 at 1:07 AM, Daniel Russ <[email protected]> wrote: >>> Jörn, >>> >>> Currently, GISTrainer has a private static final variable LLThreshold, >>> which controls if the change in the log likelihood between two iterations >>> is too small. We could make this parameter. I am concerned about using the >>> accuracy to train the model. If we use accuracy, the weight space may be >>> flat. >>> >>> Saurabh, you use the term “early stopping”. In deep learning, early >>> stopping is used to prevent overtraining and improve generalization to >>> unseen data. I am not sure early stopping serves the same purpose with GIS >>> training. Does anyone know if early stopping improves generalization for a >>> maxent problem? >>> >>> Daniel >>> >>>> On Aug 24, 2017, at 4:48 AM, Joern Kottmann <[email protected]> wrote: >>>> >>>> You are the first one who ever asked this question. I think we have this as >>>> an option already on the gis trainer but it is not exposed all the way >>>> through. >>>> >>>> Please open a jira and I can look at it next week. >>>> >>>> Jörn >>>> >>>> On Aug 21, 2017 5:11 PM, "Saurabh Jain" <[email protected]> wrote: >>>> >>>>> Hi All >>>>> >>>>> How can we use early stopping while training/crossvalidating custom data >>>>> with NameFinder ? What I want if change in likelihood value or accuracy of >>>>> model is less than 0.05 between two steps (differ by 5 i.e compare x+5 >>>>> step >>>>> output with x step) then training should stop. I could not find anything >>>>> regarding this in documentation. Can some one please help ? >>>>> >>>>> -- >>>>> *Thanks & Regards* >>>>> >>>>> >>>>> *Saurabh Jain * >>>>> *AI Developer* >>>>> >>>>> *Active Intelligence * >>>>> >>>>> *"* >>>>> *To do a thing yesterday was the best time . Second best time is today .” >>>>> * >>>>> >>> >
