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 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
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 wrote:
>
> Hi Daniel,
>
> do you see any issue if we expose LLThreshold and allow the
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 wrote:
> Jörn,
>
>Currently, GISTrainer has a private static final variable LLThreshold,
> which controls if the change in the
Hi Jorn
Let me explain you what I am trying to do. I have 3 categories and need to
create 3 different NameFinder model for each category. I train 3 models in
sequence (although each one is independent) with training step 1000. While
training on a specific category, lets say first category, if chan
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 spac
can't you set the number of iterations in the training properties
On Aug 24, 2017 4:48 AM, "Joern Kottmann" 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 j
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" wrote:
> Hi All
>
> How can we use early s
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 any
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 any
