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Classification on MNIST #141
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MNIST is a high-dimensional dataset, where pure symbolic regression is going to do quite poorly due to the combinatoric scaling. What you can try instead is something like described in https://arxiv.org/abs/2006.11287 (see interactive example of this at the end of https://colab.research.google.com/github/MilesCranmer/PySR/blob/master/examples/pysr_demo.ipynb). Basically, write down a neural network like where |
For example, maybe you'll get something like: Thus, your final equation would be: where the sum is over small patches of |
Regarding applying SymbolicRegression to high-dimensional data sets in general, I imagine the recommendation would be to start with a feature-selection approach, and once a small number of highly-relevant features is selected apply SymbolicRegression? |
I tried to use sr for mnist classification training, but the results were not good.
I hope you can help me see where I need to improve
MNIST is 28*28 images, 10 classes label.
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