Hi Klo.
Actually back then I was using libSVM directly, not scikit-learn, which
I hadn't discovered yet ;)
Also, if you actually want to do that grid-search, it might take for
ever. Why do you want to do the grid-search on MNIST again?
Andy
On 05/25/2014 07:23 AM, klo uo wrote:
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
<http://www1.i2r.a-star.edu.sg/%7Ejspan/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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