Hi Caleb,
thanks for the pointers. I'm curious what mnist dataset will give as a
result and hope for better ones.
I browsed the paper you linked, and assume you suggest I do transductive
transfer learning model - but I have no idea how to relate that to sklearn
and use it.
Currently I'm trying to fit mnist dataset, with parameters suggested by
Andy in his blog post article. I hope I understand that he is using sklearn
algorithm - grid search, to find optimal parameters, and these parameters
are those you referred as hyper parameters.
I started fitting couple of hours ago, and not sure if it will ever finish.
I'm thinking now to interrupt the process soon and randomly sample 1/10-th
of mnist dataset and try again.
I see there is model persistence function provided by sklearn, and that's
great, but I'm not sure about the time scale when this fitting process will
finish if I let it go.
About tesseract and SVM comparison - I know they are apples and oranges,
although both are parts of machine learning, I was just expressing my
results.
On Sat, May 24, 2014 at 4:31 PM, Caleb wrote:
> Do note that the digit dataset(MNIST) you used to train the classifier
> consists of hand-written digits, while the dataset you used in testing
> consists of machine generated digits. It is like learning to read English
> by learning German, it might work to some extent but not much. You might be
> interested in branch of machine learning called transfer learning which
> deals with this kind of situation. You can find a survey paper here:
> http://www1.i2r.a-star.edu.sg/~jspan/publications/TLsurvey_0822.pdf
>
> Anyhow, this might be far more work than you expect. May I know why do you
> want to compare SVM to tesseract?
>
>
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