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Kuzushiji with SVM(support vector machine) #3
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Thanks for the analysis, Tom! Let’s put these results on the GitHub
leaderboard since I think people will be interested in comparison to SVMs.
…On Mon, Dec 24, 2018 at 8:59 PM TomZephire ***@***.***> wrote:
Kuzushiji_by_SVM.zip
<https://github.com/rois-codh/kmnist/files/2707170/Kuzushiji_by_SVM.zip>
I believe inferior counterpart is necessary to feature sophisticated DNNs
more.
SVMs with 'RBF' Kernel with chosen parameters C and gamma by a random
search give the following results.
Accuracy:
KMNIST 92.82% (test data), 99.98% (training data)
K49 87.09% (test data), 97.96% (training data)
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Thank you for your advise. It is my first posting in GitHub. I have to learn and figure out how to do it... |
Thank you for your analysis. :) |
Hi @TomZephire, thanks for your analysis! I am running it now on the original MNIST dataset to get a number for that, and I will add it to our leaderboard :) One thing I noticed is that you calculate K49 accuracy incorrectly - as we suggest using balanced accuracy (accuracy averaged over classes) to combat the inbalanced dataset. I notice you mention it can take over a day to run the SVM... So if you still have it could you send test predictions for K49? Thanks |
Thank you for your comment. |
Added these! (apologies for the long delay) |
Kuzushiji_by_SVM.zip
I believe inferior counterpart is necessary to feature sophisticated DNNs more.
SVMs with 'RBF' Kernel with chosen parameters C and gamma by a random search give the following results.
Accuracy:
KMNIST 92.82% (test data), 99.98% (training data)
K49 85.61% (test data), 97.94% (training data) <--correct value(class averaged)
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